Downscaling system for modelling of atmospheric composition on regional, urban and street scales

Abstract. In this study, the downscaling modelling chain for prediction of weather and atmospheric composition is described and evaluated against observations. The chain consists of interfaced models for forecasting at different spatio-temporal scales and run in a semi-operational mode. The forecasts were performed for European (EU) regional and Danish (DK) sub-regional/urban-scales by the offline coupled numerical weather prediction HIRLAM and atmospheric chemical transport CAMx models, and for Copenhagen city/street scale – by the online coupled computational fluid dynamics M2UE model. The results showed elevated NO_x and lowered O₃ concentrations over major urban, industrial and transport land/water routes in both the EU and DK domain forecasts. The O₃ diurnal cycle predictions in both these domains were equally good, although O₃ values were closer to observations for Denmark. At the same time, the DK forecast of NO_x levels was more biased (with better prediction score of the diurnal cycle) than EU forecast, indicating a necessity to adjust emission rates. Further downscaling to the street level (Copenhagen) indicated that the NO_x pollution was 2-fold higher on weekend and more than 5 times higher during working day with high pollution episode. Despite of high uncertainty in road traffic emissions, the street-scale model captured well the NO_x diurnal cycle and onset of elevated pollution episode. The demonstrated downscaling system could be used in future online integrated meteorology and air quality research and operational forecasting, as well as applied for impact assessments on environment, population and decision making for emergency preparedness and safety measures planning.