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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.