Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1

An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These...

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Bibliographic Details
Published in:Geoscientific Model Development 2019-01, Vol.12 (1), p.33-67
Main Authors: Brasseur, Guy P, Xie, Ying, Petersen, Anna Katinka, Bouarar, Idir, Flemming, Johannes, Gauss, Michael, Jiang, Fei, Kouznetsov, Rostislav, Kranenburg, Richard, Mijling, Bas, Peuch, Vincent-Henri, Pommier, Matthieu, Segers, Arjo, Sofiev, Mikhail, Timmermans, Renske, van der A, Ronald, Walters, Stacy, Xu, Jianming, Zhou, Guangqiang
Format: Article
Language:English
Subjects:
Air
Air pollution
Air quality
Airborne particulates
Algorithms
Analysis
Boundary conditions
Chemical transport
Chronic obstructive pulmonary disease
Daily forecasts
Documentation
Emissions
Ensemble forecasting
Forecasting
Forecasts and trends
Intercomparison
Nitrogen oxides
Organic chemistry
Oxides
Ozone
Partial differential equations
Particulate matter
Photochemicals
Pollutants
Suspended particulate matter
Urban areas
Weather
Weather forecasting
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Summary:An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible website (http://www.marcopolo-panda.eu, last access: 7 December 2018). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an intercomparison of the different forecasts performed during a given period of time (1–15 March 2017) and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-12-33-2019