Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1
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Published:2019-04-02
Issue:3
Volume:12
Page:1241-1266
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Petersen Anna Katinka, Brasseur Guy P., Bouarar Idir, Flemming JohannesORCID, Gauss Michael, Jiang FeiORCID, Kouznetsov RostislavORCID, Kranenburg Richard, Mijling Bas, Peuch Vincent-HenriORCID, Pommier Matthieu, Segers Arjo, Sofiev Mikhail, Timmermans Renske, van der A RonaldORCID, Walters Stacy, Xie Ying, Xu Jianming, Zhou GuangqiangORCID
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.
Funder
European Commission
Publisher
Copernicus GmbH
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