Abstract
Abstract. In order to improve the surface ozone forecast over Beijing and surrounding regions, data assimilation method integrated into a high-resolution regional air quality model and a regional air quality monitoring network are employed. Several advanced data assimilation strategies based on ensemble Kalman filter are designed to adjust O3 initial conditions, NOx initial conditions and emissions, VOCs initial conditions and emissions separately or jointly through assimilating ozone observations. As a result, adjusting precursor initial conditions demonstrates potential improvement of the 1-h ozone forecast almost as great as shown by adjusting precursor emissions. Nevertheless, either adjusting precursor initial conditions or emissions show deficiency in improving the short-term ozone forecast at suburban areas. Adjusting ozone initial values brings significant improvement to the 1-h ozone forecast, and its limitations lie in the difficulty in improving the 1-h forecast at some urban site. A simultaneous adjustment of the above five variables is found to be able to reduce these limitations and display an overall better performance in improving both the 1-h and 24-h ozone forecast over these areas. The root mean square errors of 1-h ozone forecast at urban sites and suburban sites decrease by 51% and 58% respectively compared with those in free run. Through these experiments, we found that assimilating local ozone observations is determinant for ozone forecast over the observational area, while assimilating remote ozone observations could reduce the uncertainty in regional transport ozone.
Highlights
As one of the typical city clusters in China, Beijing and its surrounding areas are facing serious challenges in surface ozone pollutions within urbanization and motorization processes (Chan and Yao, 2008; Shao et al, 2006)
Aiming at improving the ozone forecast over Beijing and surrounding regions, this study explores several advanced data assimilation strategies designed to adjust ozone initial conditions, precursor initial conditions and precursor emission rates separately or jointly through assimilating ozone observations
The results suggest that Ensemble Kalman filter (EnKF) is a powerful tool for improving ozone forecast over Beijing and surrounding regions
Summary
As one of the typical city clusters in China, Beijing and its surrounding areas are facing serious challenges in surface ozone pollutions within urbanization and motorization processes (Chan and Yao, 2008; Shao et al, 2006). Forecasting and early warning of ozone pollution, performed during the Beijing Olympic Games to ensure a health environment for athletes and attendees, constituted an important issue for the Campaign of Air Quality Research in Beijing and the Surrounding Region (CAREBEIJING-2008, Wang et al, 2011). In previous studies on ozone forecast over Beijing, An et al (2010) and Yu et al (2011) developed statistical forecast models to forecast ozone concentrations based on several statistical techniques including multiple linear regressions, principal component analysis and neural network methods, while Tang et al (2010a) and Zhang et al (2010) employed ensemble forecast methods based on chemical transport model (CTM) to forecast ozone. The ensemble forecasting methods with CTM contain the influences from the complex chemical and dynamical processes and do not have the conceptional limitations with statistical
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