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Volume 8, issue 7 | Copyright

Special issue: The community version of the Weather Research and Forecasting...

Special issue: Coupled chemistry–meteorology modelling: status and...

Geosci. Model Dev., 8, 2119-2137, 2015
https://doi.org/10.5194/gmd-8-2119-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 16 Jul 2015

Development and technical paper | 16 Jul 2015

Evaluation of the high resolution WRF-Chem (v3.4.1) air quality forecast and its comparison with statistical ozone predictions

R. Žabkar1,2, L. Honzak2,a, G. Skok1,2, R. Forkel3, J. Rakovec1,2, A. Ceglar2,4,b, and N. Žagar1,2 R. Žabkar et al.
  • 1University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia
  • 2Center of Excellence SPACE-SI, Ljubljana, Slovenia
  • 3Karlsruher Institut für Technologie, Institut für Meteorologie und Klimaforschung, Atmosphärische Umweltforschung, Garmisch-Partenkirchen, Germany
  • 4University of Ljubljana, Biotechnical Faculty, Ljubljana, Slovenia
  • anow at: BO-MO d.o.o., Ljubljana, Slovenia
  • bnow at: Institute for Environment and Sustainability, Joint Research Centre, Ispra, Italy

Abstract. An integrated modelling system based on the regional online coupled meteorology–atmospheric chemistry WRF-Chem model configured with two nested domains with horizontal resolutions of 11.1 and 3.7 km has been applied for numerical weather prediction and for air quality forecasts in Slovenia. In the study, an evaluation of the air quality forecasting system has been performed for summer 2013. In the case of ozone (O3) daily maxima, the first- and second-day model predictions have been also compared to the operational statistical O3 forecast and to the persistence. Results of discrete and categorical evaluations show that the WRF-Chem-based forecasting system is able to produce reliable forecasts which, depending on monitoring site and the evaluation measure applied, can outperform the statistical model. For example, the correlation coefficient shows the highest skill for WRF-Chem model O3 predictions, confirming the significance of the non-linear processes taken into account in an online coupled Eulerian model. For some stations and areas biases were relatively high due to highly complex terrain and unresolved local meteorological and emission dynamics, which contributed to somewhat lower WRF-Chem skill obtained in categorical model evaluations. Applying a bias correction could further improve WRF-Chem model forecasting skill in these cases.

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