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Atmospheric Chemistry
Atmospheric Chemistry
Special Interest Group (Atmospheric Chemistry)

Atmospheric Chemistry and Data Science – The Final Frontier?

DATE

Thursday 8 December 2022
11:00 - 16:00

Add Event to Calendar: Google

LOCATION

Virtual - Hosted on Zoom

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.

Programme

Agenda

8th Dec 2022 11:00 - 16:00

Session schedule

Hide
Time Title Speaker
11:00 Digital Twins of urban air quality: opportunities and challenges. D O Topping
11:30 Downscaling an atmospheric chemistry transport model ozone surface using machine learning L Gouldsbrough
12:00 Using Automated Image Detection to Find Pollution Sources from Satellite Data D Finch
12:30 Machine learning for atmospheric chemistry: why, where, how? P J Nowack
14:00 Deep Learning for Air Quality Research: Concepts, Issues, and Benchmarks M Schultz
14:30 An Online-Learned Machine Learning Chemical Solver for Stable, Fast, and Long-Term Global Simulations of Atmospheric Chemistry M Kelp
15:00 Gaussian process emulation of the global methane budget: a sensitivity analysis
15:30 Combining chemistry-climate model ensembles and observations with Bayesian machine learning. M Amos

Registration

REGISTRATION IS CLOSED

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.

Programme

Agenda

8th Dec 2022 11:00 - 16:00

Session schedule

Hide
Time Title Speaker
11:00 Digital Twins of urban air quality: opportunities and challenges. D O Topping
11:30 Downscaling an atmospheric chemistry transport model ozone surface using machine learning L Gouldsbrough
12:00 Using Automated Image Detection to Find Pollution Sources from Satellite Data D Finch
12:30 Machine learning for atmospheric chemistry: why, where, how? P J Nowack
14:00 Deep Learning for Air Quality Research: Concepts, Issues, and Benchmarks M Schultz
14:30 An Online-Learned Machine Learning Chemical Solver for Stable, Fast, and Long-Term Global Simulations of Atmospheric Chemistry M Kelp
15:00 Gaussian process emulation of the global methane budget: a sensitivity analysis
15:30 Combining chemistry-climate model ensembles and observations with Bayesian machine learning. M Amos

Registration

REGISTRATION IS CLOSED

DATE

Thursday 8 December 2022
11:00 - 16:00

Add Event to Calendar: Google

LOCATION

Virtual - Hosted on Zoom
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