Modeling study of a severe aerosol pollution event in December 2013 over Shanghai China: An application of chemical data assimilation--颗粒学报PARTICUOLOGY

Wu, J.-B., Xu, J., Pagowski, M., Geng, F., Gu, S., Zhou, G., Xie, Y., & Yu, Z. (2015). Modeling study of a severe aerosol pollution event in December 2013 over Shanghai China: An application of chemical data assimilation. Particuology, 20, 41–51. https://doi.org/10.1016/j.partic.2014.10.008

Modeling study of a severe aerosol pollution event in December 2013 over Shanghai China: An application of chemical data assimilation

Jian-Bin Wu ^a *, Jianming Xu ^a, Mariusz Pagowski ^b, Fuhai Geng ^a, Songqiang Gu ^a, Guangqiang Zhou ^a, Ying Xie ^a, Zhongqi Yu ^a

a Shanghai Meteorological Service, Shanghai 200135, China
b NOAA Earth System Research Laboratory (ESRL), Boulder, Colorado, USA

10.1016/j.partic.2014.10.008

Volume 20, June 2015, Pages 41-51

Received 29 July 2014, Revised 16 October 2014, Accepted 21 October 2014, Available online 10 February 2015.

E-mail: wujianbin83@126.com; benwu815@gmail.com

Highlights

• We performed a modeling study on 9-day severe PM_2.5 pollution event in Shanghai on December 2013.

• 3D-variational GSI approach was used to assimilate PM_2.5 to improve initial conditions.

• The improvement in the forecasts with data assimilation was clearly noted.

• Data assimilation improved aerosol forecasts for most of the stations in East China.

Abstract

This study focuses on the importance of initial conditions to air-quality predictions. We ran assimilation experiments using the WRF-Chem model and grid-point statistical interpolation (GSI), for a 9-day severe particulate matter pollution event that occurred in Shanghai in December 2013. In this application, GSI used a three-dimensional variational approach to assimilate ground-based PM_2.5 observations into the chemical model, to obtain initial fields for the aerosol species. In our results, data assimilation significantly reduced the errors when compared to a simulation without assimilation, and improved forecasts of PM_2.5 concentrations. Despite a drop in skill directly after the assimilation, a positive effect was present in forecasts for at least 12–24 h, and there was a slight improvement in the 48-h forecasts. In addition to performing well in Shanghai, the verification statistics for this assimilation experiment are encouraging for most of the surface stations in China.

Graphical abstract

Keywords

Data assimilation; Aerosol pollutionInitial condition; Forecasting; PM_2.5

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