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Discussion papers
https://doi.org/10.5194/acp-2018-768
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-768
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Jul 2018

Research article | 30 Jul 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

The impact of multi-species surface chemical observations assimilation on the air quality forecasts in China

Zhen Peng1, Lili Lei1, Zhiquan Liu2, Jianning Sun1,3, Aijun Ding1,3, Junmei Ban2, Dan Chen4, Xingxia Kou4, and Kekuan Chu1 Zhen Peng et al.
  • 1School of Atmospheric Sciences, Nanjing University, Nanjing, China
  • 2National Center for Atmospheric Research, Boulder, Colorado, USA
  • 3Institute for Climate and Global Change Research, Nanjing University, Nanjing, China
  • 4Institute of Urban Meteorology, CMA, Beijing, China

Abstract. An Ensemble Kalman Filter data assimilation (DA) system has been developed to improve air quality forecasts using surface measurements of PM10, PM2.5, SO2, NO2, O3 and CO together with an online regional chemical transport model, WRF-Chem (Weather Research and Forecasting with Chemistry). This DA system was applied to simultaneously adjust the chemical initial conditions (ICs) and emission inputs of the species affecting PM10, PM2.5, SO2, NO2, O3 and CO concentrations during an extreme haze episode that occurred in early October 2014 over the North China Plain. Numerical experimental results indicate that ICs play key roles in PM2.5, PM10 and CO forecasts during the severe haze episode. The 72-h verification forecasts with the optimized ICs and emissions performed very similarly to the verification forecasts with only optimized ICs and the prescribed emissions. For the first-day forecast, near perfect verification forecasts results were achieved. However, with longer range forecasts, the DA impacts decayed quickly. For the SO2 verification forecasts, it was efficient to improve the SO2 forecast via the joint adjustment of SO2 ICs and emissions. Large improvements were achieved for SO2 forecasts with both the optimized ICs and emissions for the whole 72-h forecast range. Similar improvements were achieved for SO2 forecasts with optimized ICs only for just the first 3h, and then the impact of the ICs decayed quickly. For the NO2 verification forecasts, both forecasts performed much worse than the control run without DA. Plus, the 72-h O3 verification forecasts performed worse than the control run during the daytime, due to the worse performance of the NO2 forecasts, even though they performed better at night. However, relatively favorable NO2 and O3 forecast results were achieved for the Yangtze River delta and Pearl River delta regions.

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
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Zhen Peng et al.
Zhen Peng et al.
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An EnKF system was developed to simultaneously assimilate multiple surface measurements, including PM10, PM2.5, SO2, NO2, O3 and CO, via the joint adjustment of ICs and source emissions. Large improvements were achieved in the first 24 h forecast for PM2.5, PM10, SO2 and CO during an extreme haze episode that occurred in early October 2014 over the North China Plain, but no improvements were achieved for NO2 and O3.
An EnKF system was developed to simultaneously assimilate multiple surface measurements,...
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