Abstract
AQ-WATCH (Air Quality: Worldwide Analysis and Forecasting of Atmospheric Composition for Health) is an international consortium, which co-develops and co-produces tailored products and services derived from space and in situ observational data for improving air quality forecasts and attribution. For this purpose, AQ-WATCH develops a supply chain leading to innovative downstream products and services for providing air quality information tailored to the identified needs of international users. This presentation will focus on one of the AQ-WATCH products, the AQ-WATCH air quality forecast system. Air quality forecast models provided by the AQ-WATCH consortium are set up for the focus regions in Asia and the Americas, based on the templates of Copernicus European and MarcoPolo-Panda Asian ensembles, but with much higher resolution and reliance on regional emission and observational information. The models are established over the focus regions using the meteorological and emission data taken from Copernicus repositories and other national archives and refined with local information wherever available. Each forecast model is then evaluated using local observational datasets and with the needs of the stakeholders. Machine learning workflows are being incorporated into the forecast system to improve both results from individual models and the model ensembles based on bias correction from observation data. Lessons learnt from model comparison in the focus regions will be presented. At last, the potential application of the system prototype, as well as the other AQ-WATCH products, namely the global and regional air quality atlas, the air quality attribution & mitigation, the dust and fire forecasts, and the fracking analysis tool, to other regions of the world will be discussed.
Published Version
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