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Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-234
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 01 Oct 2018

Model evaluation paper | 01 Oct 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Ensemble Forecasts of Air Quality in Eastern China – Part 2. Evaluation of the MarcoPolo-Panda Prediction System, Version 1

Anna Katinka Petersen1, Guy P. Brasseur1,2, Idir Bouarar1, Johannes Flemming3, Michael Gauss4, Fei Jiang5, Rostislav Kouznetsov6, Richard Kranenburg7, Bas Mijling8, Vincent-Henri Peuch3, Matthieu Pommier4, Arjo Segers7, Mikhail Sofiev6, Renske Timmermans7, Ronald van der A8,9, Stacy Walters2, Ying Xie10, Jianming Xu10, and Guangqiang Zhou10 Anna Katinka Petersen et al.
  • 1Max Planck Institute for Meteorology, Hamburg, Germany
  • 2National Center for Atmospheric Research, Boulder, CO, USA
  • 3European Centre for Middle Range Weather Forecasts, Reading, UK
  • 4Norwegian Meteorological Institute, Oslo, Norway
  • 5Nanjing University, Nanjing, China
  • 6Finnish Meteorological Institute, Helsinki, Finland
  • 7TNO, Utrecht, the Netherlands
  • 8Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 9Nanjing University of Information Science and Technology, Nanjing, China
  • 10Shanghai Meteorological Service, Shanghai, China

Abstract. An operational multi-model 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 multi-model 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 multi-model ensemble predictions for the next 72 hours are displayed as hourly outputs on a publicly accessible web site (www.marcopolo-panda.eu).

In this paper, the performance of the predictions system (individual models and the multi-model ensemble) for the first operational year (April 2016 until June 2017) has been analysed 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 alert warning (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

Anna Katinka Petersen et al.
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Status: open (until 26 Nov 2018)
Status: open (until 26 Nov 2018)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Anna Katinka Petersen et al.
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Latest update: 18 Oct 2018
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Short summary
An operational multi-model forecasting system for air quality is providing daily forecasts of ozone, nitrogen oxides, and particulate matter for 37 urban areas of China. The paper presents the evaluation of the different forecasts performed during the first year of operation.
An operational multi-model forecasting system for air quality is providing daily forecasts of...
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