Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions

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 O 3 initial conditions, NO x 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.

Limitations of ozone data assimilation with adjustment of NO<sub><i>x</i></sub> emissions: mixed effects on NO<sub>2</sub> forecasts over Beijing and surrounding areas

“…In the field of air pollution, the EnKF has been shown to be an efficient method in optimizing concentrations. Further applications of the EnKF in improving dust and ozone forecast skills through emission optimization have been reported (e.g., Constantinescu et al, 2007;Eben et al, 2005;Lin et al, 2008;Tang et al, 2011).…”

“…Vertically, the model was set as 20 terrain-following layers, 9 of which were within the lowest 2 km of the atmosphere and the height of the first layer near the surface was 50 m. The fifth-generation Penn State National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; Grell et al, 1994) was employed to provide the hourly meteorological inputs for NAQPMS. The regional emission data of the Intercontinental Chemical Transport Experiment -Phase B (INTEX-B) Asia inventory for 2006 with 0.5 • × 0.5 • resolution (Zhang et al, 2009) and the local high-resolution emission inventory were combined to provide the emission data for NAQPMS (Tang et al, 2011).…”

“…The importance of emissions as control variables in the CDA has also been documented recently (Carmichael et al, 2008;Koohkan et al, 2013;Zhang et al, 2012). Accordingly, advanced CDA techniques that enable inverse or cross-variable adjustments of emissions have been established and their applications have provided significant improvement of ozone forecasts (e.g., Tang et al, 2011).…”

“…However, the impact of ozone assimilation on the precursor (NO 2 and volatile organic compounds) uncertainty was not elucidated. This paper (as an extension of Tang et al, 2011), based on the assimilation experiments performed by Tang et al (2011), attempts to analyze in detail the impacts of the cross-variable ozone data assimilation on NO 2 forecasts over Beijing and surrounding areas during the 2008 Beijing Olympic Games. Both real O 3 data assimilation (with a three-dimensional chemical transport model) and ideal O 3 data assimilation experiments (with a box model) are performed to investigate the state of NO 2 and NO x emissions during assimilation processes in order to provide further insights into the scientific potential of the assimilation method.…”

“…Such inconsistencies, i.e., the emissions enhanced under the overestimation of concentrations or the emissions reduced under the underestimation of concentrations, reveal some gaps between ozone forecast improvement and precursor emission optimization and call for a comprehensive evaluation of the cross-variable CDA techniques. Tang et al (2011) employed a high horizontal resolution (9 km) model to perform the assimilation of O 3 observations with the ensemble Kalman filter and the adjustment of NO x emissions for O 3 forecast improvement over Beijing and its surrounding areas. However, the impact of ozone assimilation on the precursor (NO 2 and volatile organic compounds) uncertainty was not elucidated.…”