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

Abstract

In this study, the downscaling modeling chain for prediction of weather and atmospheric composition is described and evaluated against observations. The chain consists of interfacing models for forecasting at different spatiotemporal scales that run in a semi-operational mode. The forecasts were performed for European (EU) regional and Danish (DK) subregional-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 NOx and lowered O3 concentrations over major urban, industrial, and transport land and 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 NOx and NO2 levels was more biased (with a better prediction score of the diurnal cycle) than the EU forecast, indicating a necessity to adjust emission rates. Further downscaling to the street level (Copenhagen) indicated that the NOx pollution was 2-fold higher on weekends and more than 5 times higher during the working day with high pollution episodes. Despite high uncertainty in road traffic emissions, the street-scale model effectively captured the NOx and NO2 diurnal cycles and the onset of elevated pollution episodes. The demonstrated downscaling system could be used in future online integrated meteorology and air quality research and operational forecasting, as well as for impact assessments on environment, population, and decision making for emergency preparedness and safety measures planning.