Articles | Volume 18, issue 14
https://doi.org/10.5194/amt-18-3495-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3495-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jul 2025
Research article |
| 28 Jul 2025
| 28 Jul 2025
Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Google, Mountain View, CA, USA
Vincent Meijer
Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Rémi Chevallier
Federation ENAC ISAE SUPAERO ONERA, Université de Toulouse, Toulouse, France
Allie Duncan
Google, Mountain View, CA, USA
Kyle McConnaughay
formerly at: Google, Mountain View, CA, USA
Scott Geraedts
Google, Mountain View, CA, USA
Kevin McCloskey
Google, Mountain View, CA, USA
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This randomized control trial provides evidence for the efficacy of airline-led contrail avoidance to reduce aviation's climate impact. By integrating ML forecasts into standard flight planning, dispatchers routed flights to avoid warming contrails. Satellite validation showed an 11.6 % overall reduction in contrails, increasing to 62 % for flights strictly following the optimized paths, all with no significant fuel penalty.
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It can be difficult to know if a given aircraft made a contrail. Existing methods that can be applied to any aircraft either use numerical weather data or satellite observations. In this work we create a new method by combining weather data and observations together. By comparison to in-situ measurements we show that the new method is superior to previous methods.
Tharun Sankar, Thomas Dean, Tristan Abbott, Jill Blickstein, Alejandra Martín Frías, Mark Galyen, Rebecca Grenham, Paul Hodgson, Kevin McCloskey, Alan Pechman, Tyler Robarge, Dinesh Sanekommu, Aaron Sarna, Aaron Sonabend-W, Marc Stettler, Raimund Zopp, and Scott Geraedts
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This randomized control trial provides evidence for the efficacy of airline-led contrail avoidance to reduce aviation's climate impact. By integrating ML forecasts into standard flight planning, dispatchers routed flights to avoid warming contrails. Satellite validation showed an 11.6 % overall reduction in contrails, increasing to 62 % for flights strictly following the optimized paths, all with no significant fuel penalty.
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Preprint under review for JECATS
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It can be difficult to know if a given aircraft made a contrail. Existing methods that can be applied to any aircraft either use numerical weather data or satellite observations. In this work we create a new method by combining weather data and observations together. By comparison to in-situ measurements we show that the new method is superior to previous methods.
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Airplane condensation trails trap heat, but their full warming effect is hard to measure because they blend into natural clouds. Using satellite observations, weather data, flight paths, and a causal inference framework, we isolated this effect without simulations or contrail masks. We found contrails trap 46.9 gigajoules of heat per kilometer flown over the Americas. This provides a crucially missing observation-based estimate of a major portion of aviation’s environmental impact.
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We evaluate the regional and seasonal variability in the prediction of ice supersaturated region (ISSRS) in the ERA5 reanalysis using in situ measurements. ERA5 shows better ability to predict ISSRs in the extratropics, compared to the tropics, and in colder seasons, such as extratropical winter. While ERA5 generally underestimates the ISSR occurrence, we find an overestimation in tropical regions in seasons associated larger weather variability, such as South Asia in June, July and August.
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
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Short summary
Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining which flight formed each contrail is a critical step to decreasing this impact. We introduce a dataset of synthetic contrail observations with known flight attributions that can be used to develop and assess geostationary-satellite-based contrail-to-flight attribution systems. We additionally introduce a new attribution algorithm and show that it outperforms previous methods.
Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining...
