Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1
<p>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,...
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doaj-5461c8e4cfcf417eaff028e89507db942020-11-24T21:41:06ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032019-04-01121241126610.5194/gmd-12-1241-2019Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1A. K. Petersen0G. P. Brasseur1G. P. Brasseur2I. Bouarar3J. Flemming4M. Gauss5F. Jiang6R. Kouznetsov7R. Kranenburg8B. Mijling9V.-H. Peuch10M. Pommier11A. Segers12M. Sofiev13R. Timmermans14R. van der A15R. van der A16S. Walters17Y. Xie18J. Xu19G. Zhou20Max Planck Institute for Meteorology, Hamburg, GermanyMax Planck Institute for Meteorology, Hamburg, GermanyNational Center for Atmospheric Research, Boulder, CO, USAMax Planck Institute for Meteorology, Hamburg, GermanyEuropean Centre for Medium-Range Weather Forecasts, Reading, UKNorwegian Meteorological Institute, Oslo, NorwayNanjing University, Nanjing, ChinaFinnish Meteorological Institute, Helsinki, FinlandTNO, Utrecht, the NetherlandsRoyal Netherlands Meteorological Institute (KNMI), De Bilt, the NetherlandsEuropean Centre for Medium-Range Weather Forecasts, Reading, UKNorwegian Meteorological Institute, Oslo, NorwayTNO, Utrecht, the NetherlandsFinnish Meteorological Institute, Helsinki, FinlandTNO, Utrecht, the NetherlandsRoyal Netherlands Meteorological Institute (KNMI), De Bilt, the NetherlandsNanjing University of Information Science and Technology, Nanjing, ChinaNational Center for Atmospheric Research, Boulder, CO, USAShanghai Meteorological Service, Shanghai, ChinaShanghai Meteorological Service, Shanghai, ChinaShanghai Meteorological Service, Shanghai, China<p>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.</p> <p>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 (<span class="uri">http://www.marcopolo-panda.eu</span>, last access: 27 March 2019).</p> <p>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.</p> <p>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.</p>https://www.geosci-model-dev.net/12/1241/2019/gmd-12-1241-2019.pdf |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
A. K. Petersen G. P. Brasseur G. P. Brasseur I. Bouarar J. Flemming M. Gauss F. Jiang R. Kouznetsov R. Kranenburg B. Mijling V.-H. Peuch M. Pommier A. Segers M. Sofiev R. Timmermans R. van der A R. van der A S. Walters Y. Xie J. Xu G. Zhou |
spellingShingle |
A. K. Petersen G. P. Brasseur G. P. Brasseur I. Bouarar J. Flemming M. Gauss F. Jiang R. Kouznetsov R. Kranenburg B. Mijling V.-H. Peuch M. Pommier A. Segers M. Sofiev R. Timmermans R. van der A R. van der A S. Walters Y. Xie J. Xu G. Zhou Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 Geoscientific Model Development |
author_facet |
A. K. Petersen G. P. Brasseur G. P. Brasseur I. Bouarar J. Flemming M. Gauss F. Jiang R. Kouznetsov R. Kranenburg B. Mijling V.-H. Peuch M. Pommier A. Segers M. Sofiev R. Timmermans R. van der A R. van der A S. Walters Y. Xie J. Xu G. Zhou |
author_sort |
A. K. Petersen |
title |
Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 |
title_short |
Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 |
title_full |
Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 |
title_fullStr |
Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 |
title_full_unstemmed |
Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1 |
title_sort |
ensemble forecasts of air quality in eastern china – part 2: evaluation of the marcopolo–panda prediction system, version 1 |
publisher |
Copernicus Publications |
series |
Geoscientific Model Development |
issn |
1991-959X 1991-9603 |
publishDate |
2019-04-01 |
description |
<p>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.</p>
<p>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 (<span class="uri">http://www.marcopolo-panda.eu</span>, last access: 27 March 2019).</p>
<p>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.</p>
<p>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.</p> |
url |
https://www.geosci-model-dev.net/12/1241/2019/gmd-12-1241-2019.pdf |
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