Abstract
Abstract. An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.
Highlights
IntroductionWith the rapid development of its economy, China has been experiencing repeated intense air pollution episodes (e.g., Guo et al, 2014; K. Huang et al, 2014; R.-J. Huang et al, 2014; Wang et al, 2014) with a wide range of health effects (Kampa and Castanas, 2008; Wu et al, 2012; Hamra et al, 2015; Boynard et al, 2014; WHO, 2018) and serious consequences on ecosystems (Fowler et al, 2008; Ashmore, 2005; Leisner and Ainsworth, 2012; Sinha et al, 2015) and on climate (Sitch et al, 2007; Brasseur et al, 1999; Akimoto, 2003)
In spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts of air pollution events if bias corrections are applied to improve the ozone predictions
The root mean square error (RMSE), BIAS, modified normalized bias (MNBIAS), and fractional gross error (FGE) of O3, NO2, PM10, and PM2.5 for the seven models and the ensemble median for all available observations in China are displayed over the forecasting time (0–23 h; Figs. 6 and 7)
Summary
With the rapid development of its economy, China has been experiencing repeated intense air pollution episodes (e.g., Guo et al, 2014; K. Huang et al, 2014; R.-J. Huang et al, 2014; Wang et al, 2014) with a wide range of health effects (Kampa and Castanas, 2008; Wu et al, 2012; Hamra et al, 2015; Boynard et al, 2014; WHO, 2018) and serious consequences on ecosystems (Fowler et al, 2008; Ashmore, 2005; Leisner and Ainsworth, 2012; Sinha et al, 2015) and on climate (Sitch et al, 2007; Brasseur et al, 1999; Akimoto, 2003). High concentrations of particulate matter often cover a large area of eastern China during winter when air remains stagnant for several days and chemical compounds emitted by power plants, industrial complexes, traffic, and domestic infrastructure remain trapped near the surface (e.g., Wang et al, 2014; Zhao et al, 2013). Short-term actions to avoid severe air pollution episodes, can be put in place immediately if such episodes can be reliably predicted a few days prior to their occurrence. Comprehensive air quality models that capture meteorological, chemical, and physical processes in the troposphere and predict the fate of air pollutants are key tools to forecast the likelihood of air pollution episodes and to inform the authorities
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