Time-Dependent Downscaling of PM_2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Abstract

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

Keywords

• PM_2.5
• CAMS
• Budapest
• air quality
• data fusion