高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

大气污染资料同化与应用综述

朱江 唐晓 王自发 吴林

朱江, 唐晓, 王自发, 吴林. 大气污染资料同化与应用综述[J]. 大气科学, 2018, 42(3): 607-620. doi: 10.3878/j.issn.1006-9895.1802.17260
引用本文: 朱江, 唐晓, 王自发, 吴林. 大气污染资料同化与应用综述[J]. 大气科学, 2018, 42(3): 607-620. doi: 10.3878/j.issn.1006-9895.1802.17260
Jiang ZHU, Xiao TANG, Zifa WANG, Lin WU. A Review of Air Quality Data Assimilation Methods and Their Application[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(3): 607-620. doi: 10.3878/j.issn.1006-9895.1802.17260
Citation: Jiang ZHU, Xiao TANG, Zifa WANG, Lin WU. A Review of Air Quality Data Assimilation Methods and Their Application[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(3): 607-620. 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
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

A Review of Air Quality Data Assimilation Methods and Their Application

Funds: 

National Natural Science Foundation 91644216

National Natural Science Foundation 41575128

  • 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。
  • 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
    Moore and Londergan(2001) Hanna et al.(1998) Beekmann and Derognat(2003)
    人为排放源(NOx 25%~50% 30%~40% 40%
    人为排放源(VOC) 25%~50% 50%~80% 40%
    生物排放源(NOx 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(NOx、VOC) 5%~10% 80%
    上边界条件(O3 5%~10% 70%
    上边界条件(NOx、VOC) 5%~10% 200%
    初始浓度(O3 15%~20% 30%
    初始浓度(NOx、VOC) 15%~20% 50%
    化学反应系数 25% 30% 10%~30%
    下载: 导出CSV

    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) NOx 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
    下载: 导出CSV
  • [1] Austin J. 1992. Toward the four dimensional assimilation of stratospheric chemical constituents [J]. J. Geophys. Res., 97 (D2): 2569-2588, doi: 10.1029/91JD02603.
    [2] 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. doi: 10.3321/j.issn:0250-3301.2008.02.002

    Bai Xiaoping, Li Hong, Fang Dong, et al. 2008. 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.
    [3] Barbu A L, Segers A J, Schaap M, et al. 2009. A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe [J]. Atmos. Environ., 43 (9): 1622-1631, doi:10.1016/j.atmosenv. 2008.12.005.
    [4] Beekmann M, Derognat C. 2003. Monte Carlo uncertainty analysis of a regional-scale transport chemistry model constrained by measurements from the atmospheric pollution over the Paris area (ESQUIF) campaign [J]. J. Geophys. Res., 108 (D17): 8559, doi: 10.1029/2003JD003391.
    [5] Bellouin N, Quaas J, Morcrette J J, et al. 2013. Estimates of aerosol radiative forcing from the MACC re-analysis [J]. Atmospheric Chemistry and Physics, 13 (4): 2045-2062, doi: 10.5194/acp-13-2045-2013.
    [6] Bergamaschi P, Corazza M, Karstens U, et al. 2014. Top-down estimates of European CH4 and N2O emissions based on four different inverse models [J]. Atmospheric Chemistry and Physics, 15 (2): 715-736, doi: 10.5194/acp-15-715-2015.
    [7] Bergthorsson P, Döös B R, Fryklund S, et al. 1955. Routine forecasting with the barotropic model [J]. Tellus, 7 (2): 272-274, doi:10.1111/j.2153-3490. 1955.tb01162.x.
    [8] Boersma K F, Jacob D J, Bucsela E J, et al. 2008. Validation of OMI tropospheric NO2 observations during INTEX-B and application to constrain NOx emissions over the eastern United States and Mexico [J]. Atmos. Environ., 42 (19): 4480-4497, doi:10.1016/j.atmosenv.2008.02. 004.
    [9] Bouttier F, Courtier P. 1999. Data assimilation concepts and methods [R]. Meteorological Training Course Lecture Series. ECMWF.
    [10] 曹国良, 张小曳, 龚山陵, 等. 2011.中国区域主要颗粒物及污染气体的排放源清单[J].科学通报, 56 (3): 261-268. doi: 10.1007/s11434-011-4373-7

    Cao Guoliang, Zhang Xiaoye, Gong Shanling, et al. 2011. Emission inventories of primary particles and pollutant gases for China [J]. Chinese Science Bulletin, 56 (8): 781-788, doi: 10.1007/s11434-011-4373-7.
    [11] Carmichael G R, Sandu A, Chai T F, et al. 2008. Predicting air quality: Improvements through advanced methods to integrate models and measurements [J]. J. Comput. Phys., 227 (7): 3540-3571, doi: 10.1016/j.jcp.2007.02.024.
    [12] Chang M E, Hartley D E, Cardelino C, et al. 1996. Inverse modeling of biogenic isoprene emissions [J]. Geophys. Res. Lett., 23 (21): 3007-3010, doi: 10.1029/96GL02370.
    [13] Cheng X H, Xu X D, Ding G A. 2010. An emission source inversion model based on satellite data and its application in air quality forecasts [J]. Science China Earth Sciences, 53 (5): 752-762, doi:10.1007/s11430-010- 0044-9.
    [14] Corazza M, Bergamaschi P, Vermeulen A T, et al. 2011. Inverse modelling of European N2O emissions: Assimilating observations from different networks [J]. Atmospheric Chemistry and Physics, 11 (5): 2381-2398, doi: 10.5194/acp-11-2381-2011.
    [15] Cressman G P. 1959. An operational objective analysis system [J]. Monthly Weather Review, 87 (10):367-374, doi: 10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2
    [16] 崔应杰, 王自发, 朱江, 等, 2006.空气质量数值模式预报中资料同化的初步研究[J].气候与环境研究, 11 (5): 616-626. doi: 10.3969/j.issn.1006-9585.2006.05.006

    Cui Yingjie, Wang Zifa, Zhu Jiang, et al. 2006. A preliminary study on data assimilation for numerical air quality model prediction [J]. Climatic and Environmental Research (in Chinese), 11 (5): 616-626, doi:10.3969/j.issn.1006-9585. 2006.05.006.
    [17] Dai T, Schutgens N A J, Goto D, et al. 2014. Improvement of aerosol optical properties modeling over eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model [J]. Environmental Pollution, 195: 319-329, doi:10.1016/j.envpol.2014.06. 021.
    [18] Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-interim reanalysis: Configuration and performance of the data assimilation system [J]. Quart. J. Roy. Meteor. Soc., 137 (656): 553-597, doi: 10.1002/qj.828.
    [19] Elbern H, Schmidt H, Talagrand O, et al. 2000. 4D-variational data assimilation with an adjoint air quality model for emission analysis [J]. Environmental Modelling & Software, 15 (6-7): 539-548, doi: 10.1016/S1364-8152(00)00049-9.
    [20] 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 (14): 3749-3769, doi:10.5194/acp-7-3749- 2007.
    [21] Enting I G. 2002. Inverse Problems in Atmospheric Constituent Transport [M]. Cambridge: Cambridge University Press.
    [22] Evensen G. 1994. Sequential data assimilation with a nonlinear quasi- geostrophic model using Monte Carlo methods to forecast error statistics [J]. J. Geophys. Res., 99 (C5): 10143-10162, doi: 10.1029/94JC00572.
    [23] Evensen G. 2009. The ensemble Kalman filter for combined state and parameter estimation [J]. IEEE Control Systems, 29 (3): 83-104, doi: 10.1109/MCS.2009.932223.
    [24] Fu T M, Jacob D J, Palmer P I, et al. 2007. Space-based formaldehyde measurements as constraints on volatile organic compound emissions in East and South Asia and implications for ozone [J]. J. Geophys. Res., 112 (D6): D06312, doi: 10.1029/2006JD007853.
    [25] Gandin L S. 1963. Objective Analysis of Meteorological Fields [J]. Leningrad: Gidromet. http://www.worldcat.org/title/objective-analysis-of-meteorological-fields/oclc/4629117
    [26] Gaubert B, Coman A, Foret G, et al. 2014. Regional scale ozone data assimilation using an ensemble Kalman filter and the CHIMERE chemical transport model [J]. Geoscientific Model Development Discussions, 6 (2): 3033-3083, doi: 10.5194/gmd-6-3033-2013.
    [27] Ghude S D, Kulkarni S H, Jena C, et al. 2013. Application of satellite observations for identifying regions of dominant sources of nitrogen oxides over the Indian subcontinent[J]. J. Geophys. Res., 118 (2): 1075- 1089, doi: 10.1029/2012JD017811.
    [28] Gilliland A B, Wyat Appel K, Pinder R W, et al. 2006. Seasonal NH3 emissions for the continental united states: Inverse model estimation and evaluation [J]. Atmos. Environ., 40 (26): 4986-4998, doi: 10.1016/j.atmosenv.2005.12.066.
    [29] Hanna S R, Chang J C, Fernau M E. 1998. Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-Ⅳ) due to uncertainties in input variables [J]. Atmos. Environ., 32 (21): 3619-3628, doi: 10.1016/S1352-2310(97)00419-6.
    [30] Hanna S R, Lu Z G, Frey H C, et al. 2001. Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-Ⅴ photochemical grid model applied to the July 1995 OTAG domain [J]. Atmos. Environ., 35 (5): 891-903, doi: 10.1016/S1352-2310(00)00367-8.
    [31] Hao J M, Tian H Z, Lu Y Q. 2002. Emission inventories of NOx from commercial energy consumption in China, 1995−1998 [J]. Environ. Sci. Technol., 36 (4): 552-560, doi: 10.1021/es015601k.
    [32] Hartley D, Prinn R. 1993. Feasibility of determining surface emissions of trace gases using an inverse method in a three-dimensional chemical transport model [J]. J. Geophys. Res., 98 (D3): 5183-5197, doi:10.1029/ 92JD02594.
    [33] Hayami H, Sakurai T, Han Z, et al. 2008. MICS-Asia Ⅱ: Model intercomparison and evaluation of particulate sulfate, nitrate and ammonium [J]. Atmos. Environ., 42 (15): 3510-3527, doi: 10.1016/j.atmosenv.2007.08.057.
    [34] Henze D K, Seinfeld J H, Shindell D T. 2008. Inverse modeling and mapping US air quality influences of inorganic PM2.5 precursor emissions using the adjoint of GEOS-Chem [J]. Atmospheric Chemistry and Physics Discussions, 8 (4): 15031-15099. doi: 10.5194/acpd-8-15031-2008
    [35] Hoke J E, Anthes R A. 1976. The initialization of numerical models by a dynamic-initialization technique [J]. Mon. Wea. Rev., 104 (12): 1551- 1556, doi:10.1175/1520-0493(1976)104<1551:TIONMB>2.0.CO;2.
    [36] Hooghiemstra P B, Krol M C, Bergamaschi P, et al. 2012. Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations [J]. J. Geophys. Res., 117 (D6): D06309, doi: 10.1029/2011JD017043.
    [37] 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. doi: 10.3878/j.issn.1006-9585.2016.14284

    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.
    [38] Huneeus N, Chevallier F, Boucher O. 2012. Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model [J]. Atmospheric Chemistry and Physics, 12 (10): 4585-4606, doi: 10.5194/acp-12-4585-2012.
    [39] Inness A, Baier F, Benedetti A, et al. 2013. The MACC reanalysis: An 8 yr data set of atmospheric composition [J]. Atmospheric Chemistry and Physics, 13 (8): 4073-4109, doi: 10.5194/acp-13-4073-2013.
    [40] Jiang, Z., D. B. A. Jones, M. Kopacz, et al. 2011. Quantifying the impact of model errors on top-down estimates of carbon monoxide emissions using satellite observations [J], J. Geophys. Res., 116, D15306, doi:10.1029/ 2010JD015282.
    [41] Jiang Z, Jones D B A, Worden H M, et al. 2015. Sensitivity of top-down CO source estimates to the modeled vertical structure in atmospheric CO [J]. Atmospheric Chemistry and Physics, 15 (3): 1521-1537, doi: 10.5194/acp-15-1521-2015.
    [42] Jiang Z P, Liu Z Q, Wang T J, et al. 2013. Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis [J]. J. Geophys. Res., 118 (12): 6738-6749, doi: 10.1002/jgrd.50495.
    [43] Kalman R E. 1960. A new approach to linear filtering and prediction problems [J]. Journal of Basic Engineering, 82(1): 35-45, doi:10.1115/1. 3662552.
    [44] Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The NCEP/NCAR 40-year reanalysis project [J]. Bull. Amer. Meteor. Soc., 77 (3): 437-472, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
    [45] Kinne S. 2009. Remote sensing data combinations: Superior global maps for aerosol optical depth[M]//Kokhanovsky A, De Leeuw G. Satellite Aerosol Remote Sensing over Land. Berlin, Heidelberg: Springer, 361-381, doi: 10.1007/978-3-540-69397-0.
    [46] Kistler R E. 1974. A study of data assimilation techniques in an autobarotropic, primitive equation, channel model [D]. M. S. thesis, Pennsylvania State University.
    [47] Koohkan M R, Bocquet M, Roustan Y, et al. 2013. Estimation of volatile organic compound emissions for Europe using data assimilation [J]. Atmospheric Chemistry and Physics, 13 (12): 5887-5905, doi: 10.5194/acp-13-5887-2013.
    [48] Kukkonen J, Olsson T, Schultz D M, et al. 2012. A review of operational, regional-scale, chemical weather forecasting models in Europe [J]. Atmospheric Chemistry and Physics, 12 (1): 1-87, doi:10.5194/acp- 12-1-2012.
    [49] Lin C, Wang Z, Zhu J. 2008a. An ensemble Kalman filter for severe dust storm data assimilation over China [J]. Atmospheric Chemistry and Physics, 8 (11): 2975-2983, doi: 10.5194/acp-8-2975-2008.
    [50] Lin C Y, Zhu J, Wang Z F. 2008b. Model bias correction for dust storm forecast using ensemble Kalman filter [J]. J. Geophys. Res., 113 (D14): D14306, doi: 10.1029/2007JD009498.
    [51] Liu J, Han Y Q, Tang X, et al. 2016. Estimating adult mortality attributable to PM2.5 exposure in China with assimilated PM2.5 concentrations based on a ground monitoring network [J]. Science of the Total Environment, 568: 1253-1262, doi: 10.1016/j.scitotenv.2016.05.165.
    [52] Lin J T, Nielsen C P, Zhao Y, et al. 2010. Recent changes in particulate air pollution over China observed from space and the ground: Effectiveness of emission control [J]. Environ. Sci. Technol., 44 (20): 7771-7776, doi: 10.1021/es101094t.
    [53] Liu P, Chen H. 2010. Research on the application of lidar extinction coefficient inversion method[C]//Proceedings of SPIE 7656, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment. Dalian, China: SPIE, 76565L, doi: 10.1117/12.866050.
    [54] Liu Z Q, Liu Q H, Lin H C, et al. 2011. Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia [J]. J. Geophys. Res., 116 (D23): D23206, doi: 10.1029/2011JD016159.
    [55] Lorenz E N, Emanuel K A. 1998. Optimal sites for supplementary weather observations: Simulation with a small model [J]. J. Atmos. Sci., 55 (3): 399-414, doi:10.1175/1520-0469(1998)055<0399:OSFSWO>2.0.CO;2.
    [56] 麻巨慧, 朱跃建, 王盘兴, 等. 2011. NCEP、ECMWF及CMC全球集合预报业务系统发展综述[J].大气科学学报, 34 (3): 370-380. doi: 10.3969/j.issn.1674-7097.2011.03.015

    Ma Juhui, Zhu Yuejian, Wang Panxing, et al. 2011. A review on the developments of NCEP, ECMWF and CMC global ensemble forecast system [J]. Trans. Atmos. Sci. (in Chinese), 34 (3): 370-380, doi: 10.3969/j.issn.1674-7097.2011.03.015.
    [57] Manning A J, O'Doherty S, Jones A R, et al. 2011. Estimating UK methane and nitrous oxide emissions from 1990 to 2007 using an inversion modeling approach [J]. J. Geophys. Res., 116 (D2): D02305, doi:10.1029/ 2010JD014763.
    [58] Martin R V, Jacob D J, Chance K, et al. 2003. Global inventory of nitrogen oxide emissions constrained by space-based observations of NO2 columns [J]. J. Geophys. Res., 108 (D17): 4537, doi: 10.1029/2003JD003453.
    [59] Martin, R.V. 2008. Satellite remote sensing of surface air quality [J]. Atmos. Environ., 42 (34): 7823-7843, doi: 10.1016/j.atmosenv.2008.07.018
    [60] Mijling B, Van Der A R J. 2012. Using daily satellite observations to estimate emissions of short-lived air pollutants on a mesoscopic scale [J]. J. Geophys. Res., 117 (D17): D17302, doi: 10.1029/2012JD017817.
    [61] Miyazaki K, Eskes H J, Sudo K, et al. 2012. Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions [J]. Atmospheric Chemistry and Physics Discussions, 12 (7): 16131-16218, doi:10.5194/acpd-12-16131- 2012.
    [62] Moore G E, Londergan R J. 2001. Sampled Monte Carlo uncertainty analysis for photochemical grid models[J]. Atmos. Environ., 35 (28): 4863-4876, doi: 10.1016/S1352-2310(01)00260-6.
    [63] Napelenok S L, Pinder R W, Gilliland A B, et al. 2008. A method for evaluating spatially-resolved NOx emissions using Kalman filter inversion, direct sensitivities, and space-based NO2 observations[J]. Atmospheric Chemistry and Physics, 8 (18): 5603-5614, doi: 10.5194/acp-8-5603-2008.
    [64] Nirala M. 2008. Technical note: Multi-sensor data fusion of aerosol optical thickness [J]. Int. J. Remote Sens., 29 (7): 2127-2136, doi:10.1080/ 01431160701395336.
    [65] Pagowski, M. and Grell G A. 2012. Experiments with the assimilation of fine aerosols using an ensemble Kalman filter [J]. Journal of Geophysical Research-Atmospheres 117: 15, doi: 10.1029/2012JD018333.
    [66] Pagowski M, Liu Z, Grell G A, et al. 2014. Implementation of aerosol assimilation in gridpoint statistical interpolation (v.3.2) and WRF-Chem (v.4.3.1) [J]. Geoscientific Model Development, 7 (4): 1621-1627, doi: 10.5194/gmd-7-1621-2014.
    [67] Palmer T N, Gelaro R, Barkmeijer J, et al. 1998. Singular vectors, metrics, and adaptive observations[J]. J. Atmos. Sci., 55 (4): 633-653, doi:10. 1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2.
    [68] Parrish D F, Derber J C. 1992. The national meteorological center's spectral statistical-interpolation analysis system [J]. Mon. Wea. Rev., 120 (8): 1747- 1763, doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2.
    [69] Peng Z, Liu Z, Chen D, et al. 2017. Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter [J]. Atmospheric Chemistry and Physics, 17 (7): 4837-4855, doi: 10.5194/acp-17-4837-2017.
    [70] Robertson L, Persson C. 1992. On the application of four dimensional data assimilation of air pollution data using the ajoint technique [M]//Van Dop H, Kallos G. Air Pollution Modeling and Its Application IX. Boston, MA: Springer, 365-373, doi: 10.1007/978-1-4615-3052-7_37.
    [71] Roselle S J. 1994. Effects of biogenic emission uncertainties on regional photochemical modeling of control strategies [J]. Atmos. Environ., 28 (10): 1757-1772, doi: 10.1016/1352-2310(94)90138-4.
    [72] Sandu A, Chai T F. 2011. Chemical data assimilation—An overview [J]. Atmosphere, 2 (3): 426-463, doi: 10.3390/atmos2030426.
    [73] Sasaki Y. 1970. Some basic formalisms in numerical variational analysis [J]. Mon. Wea. Rev., 98 (12): 875-883, doi:10.1175/1520-0493(1970)098<0875:SBFINV>2.3.CO; 2.
    [74] Sekiyama T T, Tanaka T Y, Shimizu A, et al. 2010. Data assimilation of CALIPSO aerosol observations[J]. Atmospheric Chemistry and Physics, 10 (1): 39-49, doi: 10.5194/acp-10-39-2010.
    [75] Sillman S, Al-Wali K I, Marsik F J, et al. 1995. Photochemistry of ozone formation in Atlanta, GA—Models and measurements [J]. Atmos. Environ., 29 (21): 3055-3066, doi: 10.1016/1352-2310(95)00217-M.
    [76] Sistla G, Zhou N, Hao W, et al. 1996. Effects of uncertainties in meteorological inputs on urban airshed model predictions and ozone control strategies [J]. Atmos. Environ., 30 (12): 2011-2025, doi:10.1016/ 1352-2310(95)00268-5.
    [77] Sportisse B. 2007. A review of current issues in air pollution modeling and simulation [J]. Computational Geosciences, 11 (2): 159-181, doi:10. 1007/s10596-006-9036-4.
    [78] Stavrakou T, Müller J F, De Smedt I, et al. 2009. The continental source of glyoxal estimated by the synergistic use of spaceborne measurements and inverse modelling [J]. Atmospheric Chemistry and Physics, 9 (21): 8431-8446, doi: 10.5194/acp-9-8431-2009.
    [79] Streets D G, Bond T C, Carmichael G R, et al. 2003. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000 [J]. J. Geophys. Res., 108 (D21): 8809, doi: 10.1029/2002JD003093.
    [80] Sugimoto N, Hara Y, Yumimoto K, et al. 2010. Dust emission estimated with an assimilated dust transport model using lidar network data and vegetation growth in the Gobi desert in Mongolia [J]. SOLA, 6 (1): 125-128, doi: 10.2151/sola.2010-032.
    [81] 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, 11 (24): 12901-12916, doi: 10.5194/acp-11-12901-2011.
    [82] 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.
    [83] Tang X, Zhu J, Wang Z F, et al. 2016. Limitations of ozone data assimilation with adjustment of NOx emissions: Mixed effects on NO2 forecasts over Beijing and surrounding areas [J]. Atmospheric Chemistry and Physics, 16 (10): 6395-6405, doi: 10.5194/acp-16-6395-2016.
    [84] 陶金花, 张美根, 陈良富, 等. 2013.一种基于卫星遥感AOT估算近地面颗粒物的方法[J].中国科学:地球科学, 43 (10): 143-154. doi: 10.1007/s11430-012-4503-3

    Tao Jinhua, Zhang Meigen, Chen Liangfu, et al. 2013. A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness [J]. Science China Earth Sciences, 56 (8): 1422-1433, doi: 10.1007/s11430-012-4503-3.
    [85] Tong M J, Xue M. 2008. Simultaneous estimation of microphysical parameters and atmospheric state with simulated radar data and ensemble square root Kalman filter. Part Ⅰ: Sensitivity analysis and parameter identifiability [J]. Mon. Wea. Rev., 136 (5): 1630-1648, doi:10.1175/ 2007MWR2070.1.
    [86] Uzunoglu B. 2007. Adaptive observations in ensemble data assimilation [J]. Computer Methods in Applied Mechanics and Engineering, 196 (41-44): 4207-4221, doi: 10.1016/j.cma.2007.04.004.
    [87] 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.
    [88] Wang M, Shao M, Chen W, et al. 2014. A temporally and spatially resolved validation of emission inventories by measurements of ambient volatile organic compounds in Beijing, China [J]. Atmospheric Chemistry and Physics, 14 (12): 5871-5891, doi: 10.5194/acp-14-5871-2014.
    [89] Wang P, Wang H, Wang Y Q, et al. 2016. Inverse modeling of black carbon emissions over China using ensemble data assimilation [J]. Atmospheric Chemistry and Physics, 16 (2): 989-1002, doi: 10.5194/acp-16-989-2016.
    [90] Wang Y, Sartelet K N, Bocquet M, et al. 2013. Assimilation of ground versus lidar observations for PM10 forecasting [J]. Atmospheric Chemistry and Physics, 13 (1): 269-283, doi: 10.5194/acp-13-269-2013.
    [91] 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, 28 (3): 353-363. doi: 10.3969/j.issn.1000-3045.2013.03.009

    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), 28 (3): 353-363, doi: 10.3969/j.issn.1000-3045.2013.03.009.
    [92] Wang Y X, McElroy M B, Martin R V, et al. 2007. Seasonal variability of NOx emissions over east China constrained by satellite observations: Implications for combustion and microbial sources [J]. J. Geophys. Res., 112 (D6): D06301, doi: 10.1029/2006JD007538.
    [93] 魏巍, 王书肖, 郝吉明. 2011.中国人为源VOC排放清单不确定性研究[J].环境科学, 32 (2): 305-312. doi: 10.13227/j.hjkx.2011.02.021

    Wei Wei, Wang Shuxiao, Hao Jiming. 2011. Uncertainty analysis of emission inventory for volatile organic compounds from anthropogenic sources in China [J]. Environmental Science (in Chinese), 32 (2): 305-312, doi: 10.13227/j.hjkx.2011.02.021.
    [94] Wu L, Mallet V, Bocquet M, et al. 2008. A comparison study of data assimilation algorithms for ozone forecasts [J]. J. Geophys. Res., 113 (D20): D20310, doi: 10.1029/2008JD009991.
    [95] 徐祥德, 许建民, 王继志, 等. 2003.大气遥感再分析场构造技术与原理[M].北京:气象出版社.

    Xu Xiangde, Xu Jianmin, Wang Jizhi, et al. 2003. The Technology and Principle of Atmospheric Remote Sensing Reanalysis Field(in Chinese) [M]. Beijing: China Meteorological Press.
    [96] 徐祥德, 周丽, 周秀骥, 等. 2004.城市环境大气重污染过程周边源影响域[J].中国科学D辑地球科学, 34 (10): 958-966. doi: 10.3969/j.issn.1674-7240.2004.10.009

    Xu Xiangde, Zhou Li, Zhou Xiuji, et al. 2004. Influencing domain of peripheral sources in the urban heavy pollution process of Beijing [J]. Science in China Series D Earth Sciences, 48 (4): 565-575, doi:10.3969/j.issn.1674- 7240.2004.10.009.
    [97] Yang Q, Wang Y H, Zhao C, et al. 2011. NOX emission reduction and its effects on ozone during the 2008 Olympic Games [J]. Environ. Sci. Technol., 45 (15): 6404-6410, doi: 10.1021/es200675v.
    [98] 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.
    [99] Yumimoto K, Nagao T M, Kikuchi M, et al. 2016. Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite [J]. Geophys. Res. Lett., 43 (11): 5886-5894, doi: 10.1002/2016GL069298.
    [100] Zhang J L, Campbell J R, Reid J S, et al. 2011. Evaluating the impact of assimilating CALIOP-derived aerosol extinction profiles on a global mass transport model [J]. Geophys. Res. Lett., 38 (14): L14801, doi: 10.1029/2011GL047737.
    [101] 张金谱, 胡嘉镗, 王雪梅. 2014.集合最优插值同化方法在珠三角空气质量模拟中的初步应用[J].环境科学学报, 34 (3): 558-566. doi: 10.13671/j.hjkxxb.2014.0103

    Zhang Jinpu, Hu Jiatang, Wang Xuemei. 2014. Preliminary application of ensemble optimal interpolation data assimilation method on air quality numerical modeling in the Pearl River Delta [J]. Acta Scientiae Circumstantiae (in Chinese), 34 (3): 558-566, doi:10.13671/j.hjkxxb. 2014.0103.
    [102] Zhang L, Shao J Y, Lu X, et al. 2016. Sources and processes affecting fine particulate matter pollution over North China: An adjoint analysis of the Beijing APEC period [J]. Environ. Sci. Technol., 50 (16): 8731-8740, doi: 10.1021/acs.est.6b03010.
    [103] Zhang Q, Streets D G, Carmichael G R, et al. 2009. Asian emissions in 2006 for the NASA INTEX-B mission [J]. Atmospheric Chemistry and Physics, 9 (14): 5131-5153, doi: 10.5194/acp-9-5131-2009.
    [104] Zhang X L, Li Q B, Su G F, et al. 2015. Ensemble-based simultaneous emission estimates and improved forecast of radioactive pollution from nuclear power plant accidents: Application to ETEX tracer experiment [J]. Journal of Environmental Radioactivity, 142: 78-86, doi: 10.1016/j.jenvrad.2015.01.013.
    [105] 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. doi: 10.1360/972013-150

    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.
    [106] Zhang Y, Bocquet M, Mallet V, et al. 2012. Real-time air quality forecasting. Part Ⅱ: State of the science, current research needs, and future prospects [J]. Atmos. Environ., 60: 656-676, doi:10.1016/j.atmosenv.2012.02. 041.
    [107] Zheng B, Zhang Q, Zhang Y, et al. 2015. Heterogeneous chemistry: A mechanism missing in current models to explain secondary inorganic aerosol formation during the January 2013 haze episode in North China [J]. Atmospheric Chemistry and Physics, 15 (4): 2031-2049, doi: 10.5194/acp-15-2031-2015.
    [108] Zheng D Q, Leung J K C, Lee B Y. 2009. Online update of model state and parameters of a Monte Carlo atmospheric dispersion model by using ensemble Kalman filter [J]. Atmos. Environ., 43 (12): 2005-2011, doi: 10.1016/j.atmosenv.2009.01.014.
    [109] 朱江, 汪萍. 2006.集合卡尔曼平滑和集合卡尔曼滤波在污染源反演中的应用[J].大气科学, 30 (5): 871-882. doi: 10.3878/j.issn.1006-9895.2006.05.16

    Zhu Jiang, Wang Ping. 2006. Ensemble Kalman smoother and ensemble Kalman filter approaches to the joint air quality state and emission estimation problem [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 30 (5): 871-882, doi: 10.3878/j.issn.1006-9895.2006.05.16.
    [110] Zoogman P, Jacob D J, Chance K, et al. 2014. Improved monitoring of surface ozone by joint assimilation of geostationary satellite observations of ozone and CO [J]. Atmospheric Environment, 84 (2):254-261, doi: 10.1016/j.atmosenv.2013.11.048
    [111] Zyryanov D, Foret G, Eremenko M, et al. 2011. 3-D evaluation of tropospheric ozone simulations by an ensemble of regional chemistry transport model [J]. Atmospheric Chemistry and Physics Discussions, 11 (10): 28797-28849, doi: 10.5194/acpd-11-28797-2011.
  • 加载中
计量
  • 文章访问数:  2280
  • HTML全文浏览量:  111
  • PDF下载量:  2242
  • 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

目录

    /

    返回文章
    返回