Primary Convener:
Zhen Qu, North Carolina State University Raleigh

Convener:
Daniel Varon, Harvard University
Makoto Kelp, Stanford University
Sam Silva, University of Southern California

Chair:
Zhen Qu, University of Colorado
Daniel Varon, Harvard University
Makoto Kelp, University of Washington Seattle Campus
Sam Silva, University of Southern California

Statistical learning and advanced computational methods are increasingly being applied to problems in atmospheric chemistry. These methodological advances combined with increasing satellite and in-situ observations provide new opportunities to improve model predictions and our understanding of atmospheric processes. This session aims to provide a forum for research on data-driven techniques for understanding the sources and concentrations of air pollutants (NOx, SOx, CO, O3, PM2.5, etc.) and greenhouse gasses (CO2, CH4, N2O, etc.). We encourage submissions on both the application of data-driven methods for interpreting new atmospheric composition datasets and the development of new methods in machine learning, statistical inference, data assimilation, and cloud computing.

Index Terms
0305 Aerosols and particles
0345 Pollution: urban and regional
0365 Troposphere: composition and chemistry
0368 Troposphere: constituent transport and chemistry

Suggested Itineraries:
Machine Learning and AI

Neighborhoods:
3. Earth Covering