Atmos. Chem. Phys. Discuss., 13, 581-631, 2013
www.atmos-chem-phys-discuss.net/13/581/2013/
doi:10.5194/acpd-13-581-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
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This discussion paper has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP.
E pluribus unum: ensemble air quality predictions
S. Galmarini1, I. Kioutsioukis1,2, and E. Solazzo1
1European Commission, Joint Research Center, Institute for Environment and Sustainability, Ispra (VA), Italy
2Region of Central Macedonia, Thessaloniki, Greece

Abstract. In this study we present a novel approach for improving the air quality predictions using an ensemble of air quality models generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). The development of the forecasting method makes use of modeled and observed time series (either spatially aggregated or relative to single monitoring stations) of ozone concentrations over different areas of Europe and North America. The technique considers the underlying forcing mechanisms on ozone by means of spectrally decomposed previsions. With the use of diverse applications we demonstrate how the approach screens the ensemble members, extracts the best components and generates bias-free forecasts with improved accuracy over the candidate models. Compared to more traditional forecasting methods such as the ensemble median, the approach reduces the forecast error and at the same time it clearly improves the modelled variance. Furthermore, the result is not a mere statistical outcome depended on the quality of the selected members. The few individual cases with degraded performance are also identified and analyzed. Finally, we show the extensions of the approach to other pollutants, specifically particulate matter and nitrogen dioxide, and provide a framework for its operational implementation.

Citation: Galmarini, S., Kioutsioukis, I., and Solazzo, E.: E pluribus unum: ensemble air quality predictions, Atmos. Chem. Phys. Discuss., 13, 581-631, doi:10.5194/acpd-13-581-2013, 2013.
 
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