Atmospheric Chemistry and Data Science – The Final Frontier?

Convenors: Paul Griffiths, University of Cambridge and Ryan Hossaini, Lancaster University

Atmospheric science is being transformed by the growing use of innovative data science and machine learning methods. In this ACSG meeting we explored how such methods have been successfully applied in atmospheric chemistry research to provide new insights into processes affecting air quality and climate. Focused on application as well as method, our national and international speakers covered topics that included (1) downscaling of atmospheric models with machine learning for improved air quality exposure studies, (2) how causal discovery algorithms offer a novel approach for climate model evaluation, and (3) how the development of ‘digital twins’ might operationally be used to improve air quality.