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

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

doi:10.5194/gmd-19-1893-2026

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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Month	HTML	PDF	XML	Total
Jul 2025	278	46	14	338
Aug 2025	1,058	54	22	1,134
Sep 2025	3,632	40	20	3,692
Oct 2025	226	50	14	290
Nov 2025	188	102	10	300
Dec 2025	122	166	12	300
Jan 2026	128	176	2	306
Feb 2026	214	146	16	376
Mar 2026	452	407	15	874
Apr 2026	228	186	7	421
May 2026	223	153	12	388
Jun 2026	45	32	7	84
Jul 2026	158	76	11	245
Aug 2026	56	67	20	143
Sep 2026	215	141	60	416

Cumulative views and downloads (calculated since 16 Jul 2025)

Month	HTML	PDF	XML	Total
Jul 2025	278	46	14	338
Aug 2025	1,058	54	22	1,134
Sep 2025	3,632	40	20	3,692
Oct 2025	226	50	14	290
Nov 2025	188	102	10	300
Dec 2025	122	166	12	300
Jan 2026	128	176	2	306
Feb 2026	214	146	16	376
Mar 2026	452	407	15	874
Apr 2026	228	186	7	421
May 2026	223	153	12	388
Jun 2026	45	32	7	84
Jul 2026	158	76	11	245
Aug 2026	56	67	20	143
Sep 2026	215	141	60	416

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Total article views: 9,307 (including HTML, PDF, and XML) Thereof 9,270 with geography defined and 37 with unknown origin.

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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.