Articles | Volume 17, issue 7
https://doi.org/10.5194/acp-17-4837-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-4837-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Improving PM2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter
Zhen Peng
CORRESPONDING AUTHOR
School of Atmospheric Sciences, Nanjing University, Nanjing, China
National Center for Atmospheric Research, Boulder, Colorado, USA
National Center for Atmospheric Research, Boulder, Colorado, USA
National Center for Atmospheric Research, Boulder, Colorado, USA
Institute of Urban Meteorology, CMA, Beijing, China
Junmei Ban
National Center for Atmospheric Research, Boulder, Colorado, USA
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Latest update: 13 Nov 2024
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.
In order to improve the forecasting of atmospheric aerosols over China, the ensemble square root...
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