Articles | Volume 17, issue 7
Atmos. Chem. Phys., 17, 4837–4855, 2017
https://doi.org/10.5194/acp-17-4837-2017
Atmos. Chem. Phys., 17, 4837–4855, 2017
https://doi.org/10.5194/acp-17-4837-2017

Research article 13 Apr 2017

Research article | 13 Apr 2017

Improving PM2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter

Zhen Peng et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Zhen Peng on behalf of the Authors (02 Dec 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (05 Dec 2016) by Toshihiko Takemura
RR by Anonymous Referee #1 (17 Dec 2016)
RR by Anonymous Referee #3 (23 Jan 2017)
ED: Reconsider after minor revisions (Editor review) (25 Jan 2017) by Toshihiko Takemura
AR by Zhen Peng on behalf of the Authors (07 Feb 2017)  Author's response    Manuscript
ED: Reconsider after minor revisions (Editor review) (24 Feb 2017) by Toshihiko Takemura
AR by Zhen Peng on behalf of the Authors (28 Feb 2017)  Author's response    Manuscript
ED: Publish as is (22 Mar 2017) by Toshihiko Takemura
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Short summary
In order to improve the forecasting of atmospheric aerosols over China, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions and primary and precursor emissions. This system was applied to assimilate hourly surface PM2.5 measurements. The forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment without data assimilation.
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