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Volume 16, issue 2
Atmos. Chem. Phys., 16, 989-1002, 2016
https://doi.org/10.5194/acp-16-989-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Haze-fog forecasts and near real time (NRT) data application...

Atmos. Chem. Phys., 16, 989-1002, 2016
https://doi.org/10.5194/acp-16-989-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Jan 2016

Research article | 27 Jan 2016

Inverse modeling of black carbon emissions over China using ensemble data assimilation

P. Wang, H. Wang, Y. Q. Wang, X. Y. Zhang, S. L. Gong, M. Xue, C. H. Zhou, H. L. Liu, X. Q. An, T. Niu, and Y. L. Cheng P. Wang et al.
  • Institute of Atmospheric Composition, Key Laboratory of Atmospheric Chemistry (LAC) of China Meteorological Administration (CMA), Chinese Academy of Meteorological Sciences (CAMS), Beijing, 100081, China

Abstract. Emissions inventories of black carbon (BC), which are traditionally constructed using a bottom-up approach based on activity data and emissions factors, are considered to contain a large level of uncertainty. In this paper, an ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. An inverse modeling system for emissions is established for an atmospheric chemistry aerosol model and two key problems related to ensemble data assimilation in the top-down emissions estimation are discussed: (1) how to obtain reasonable ensembles of prior emissions and (2) establishing a scheme to localize the background-error matrix. An experiment involving 1-year-long simulation cycle with EnOI inversion of BC emissions is performed for 2008. The bias of the BC emissions intensity in China at each grid point is corrected by this inverse system. The inverse emission over China in January is 240.1Gg, and annual emission is about 2539.3Gg, which is about 1.8 times of bottom-up emission inventory. The results show that, even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. Using top-down emissions, the average root mean square error of simulated daily BC is decreased by nearly 30%. These results are valuable and promising for a better understanding of aerosol emissions and distributions, as well as aerosol forecasting.

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An ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the possibility of optimally recovering the spatially resolved emissions bias of BC. The inversed emission over China in January is 240.1 Gg, and annual emission is about 2539 Gg. Even though only monthly mean BC measurements are employed to inverse the emissions, the accuracy of the daily model simulation improves. We finds that EnOI is a useful and computation-free method to make top-down estimation.
An ensemble optimal interpolation (EnOI) data assimilation technique is used to investigate the...
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