Articles | Volume 18, issue 14
https://doi.org/10.5194/amt-18-3495-2025
https://doi.org/10.5194/amt-18-3495-2025
Research article
 | 
28 Jul 2025
Research article |  | 28 Jul 2025

Benchmarking and improving algorithms for attributing satellite-observed contrails to flights

Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025,https://doi.org/10.5194/amt-18-3287-2025, 2025
Short summary
Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025,https://doi.org/10.5194/amt-18-3095-2025, 2025
Short summary
Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025,https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025,https://doi.org/10.5194/amt-18-2083-2025, 2025
Short summary
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025,https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary

Cited articles

Agarwal, A., Meijer, V. R., Eastham, S. D., Speth, R. L., and Barrett, S. R. H.: Reanalysis-driven simulations may overestimate persistent contrail formation by 100 %–250 %, Environ. Res. Lett., 17, 014045, https://doi.org/10.1088/1748-9326/ac38d9, 2022. a, b, c, d
Akenine-Moller, T., Haines, E., and Hoffman, N.: Real-time rendering, AK Peters/crc Press, https://doi.org/10.1201/9781315365459, 2019. a
Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R. J., Lax, R., McVeety, S., Mills, D., Perry, F., Schmidt, E., and Whittle, S.: The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing, Proc. VLDB Endow., 8, 1792–1803, https://doi.org/10.14778/2824032.2824076, 2015. a
Apache Software Foundation: Apache Beam: An advanced unified programming model, https://beam.apache.org/documentation/programming-guide/ (last access: 6 September), 2024. a
Beer, A.: Bestimmung der Absorption des rothen Lichts in farbigen Flüssigkeiten, Ann. Phys., 162, 78–88, https://doi.org/10.1002/andp.18521620505, 1852. a, b
Download
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.
Share