Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 17, 4837-4855, 2017
http://www.atmos-chem-phys.net/17/4837/2017/
doi:10.5194/acp-17-4837-2017
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
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 Peng1,2, Zhiquan Liu2, Dan Chen2,3, and Junmei Ban2 1School of Atmospheric Sciences, Nanjing University, Nanjing, China
2National Center for Atmospheric Research, Boulder, Colorado, USA
3Institute of Urban Meteorology, CMA, Beijing, China
Abstract. In an attempt to improve the forecasting of atmospheric aerosols, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions (ICs) and emission input. The forecast model, which was expanded by combining the Weather Research and Forecasting with Chemistry (WRF-Chem) model and a forecast model of emission scaling factors, generated both chemical concentration fields and emission scaling factors. The forecast model of emission scaling factors was developed by using the ensemble concentration ratios of the WRF-Chem forecast chemical concentrations and also the time smoothing operator. Hourly surface fine particulate matter (PM2. 5) observations were assimilated in this system over China from 5 to 16 October 2014. A series of 48 h forecasts was then carried out with the optimized initial conditions and emissions on each day at 00:00 UTC and a control experiment was performed without data assimilation. In addition, we also performed an experiment of pure assimilation chemical ICs and the corresponding 48 h forecasts experiment for comparison. The results showed that the forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment. In the Yangtze River delta (YRD) and the Pearl River delta (PRD) regions, large reduction of the root-mean-square errors (RMSEs) was obtained for almost the entire 48 h forecast range attributed to assimilation. In particular, the relative reduction in RMSE due to assimilation was about 37.5 % at nighttime when WRF-Chem performed comparatively worse. In the Beijing–Tianjin–Hebei (JJJ) region, relatively smaller improvements were achieved in the first 24 h forecast but then no improvements were achieved afterwards. Comparing to the forecasts with only the optimized ICs, the forecasts with the joint adjustment were always much better during the night in the PRD and YRD regions. However, they were very similar during daytime in both regions. Also, they performed similarly for almost the entire 48 h forecast range in the JJJ region.

Citation: Peng, Z., Liu, Z., Chen, D., and Ban, J.: Improving PM2. 5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter, Atmos. Chem. Phys., 17, 4837-4855, doi:10.5194/acp-17-4837-2017, 2017.
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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.
In order to improve the forecasting of atmospheric aerosols over China, the ensemble square root...
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