Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 5
Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-1893-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1893-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 5
Development and technical paper
 | 
05 Mar 2026
Development and technical paper |  | 05 Mar 2026

Enabling fast greenhouse gas emissions inference from satellites with GATES: a Graph-Neural-Network Atmospheric Transport Emulation System

Elena Fillola, Raul Santos-Rodriguez, Rachel Tunnicliffe, Jeffrey N. Clark, Nawid Keshtmand, Anita Ganesan, and Matthew Rigby

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Short summary
Satellite-based greenhouse gas measurements can be used in “inverse models” to improve emissions reporting, but one of the key components, the simulations of atmospheric transport, struggle to scale to large datasets. We introduce the model GATES, an AI-driven emulator that outputs transport plumes 1000× faster than traditional models. Applied to Brazil’s methane emissions, GATES produces estimates consistent with physics-based methods, offering a scalable path for timely emissions monitoring.
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