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.
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
Related authors
No articles found.
Katarina Grubbe Hildebrandt, Federica Castino, Vincent Meijer, and Feijia Yin
EGUsphere, https://doi.org/10.5194/egusphere-2025-3048, https://doi.org/10.5194/egusphere-2025-3048, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
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.
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
Bogacki, P. and Shampine, L.: A 3(2) pair of Runge – Kutta formulas, Appl. Math. Lett., 2, 321–325, https://doi.org/10.1016/0893-9659(89)90079-7, 1989. a
Borella, A., Boucher, O., Shine, K. P., Stettler, M., Tanaka, K., Teoh, R., and Bellouin, N.: The importance of an informed choice of CO2-equivalence metrics for contrail avoidance, Atmos. Chem. Phys., 24, 9401–9417, https://doi.org/10.5194/acp-24-9401-2024, 2024. a
Cameron, A. C., Gelbach, J. B., and Miller, D. L.: Bootstrap-Based Improvements for Inference with Clustered Errors, Rev. Econ. Stat., 90, 414–427, https://doi.org/10.1162/rest.90.3.414, 2008. a
Carver, R. W. and Merose, A.: ARCO-ERA5: An Analysis-Ready Cloud-Optimized Reanalysis Dataset, in: 103rd AMS Annual Meeting, AMS, 9 January 2023, Denver, CO, USA, 4A.1, https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/415842 (last access: 24 July 2025), 2023. a
Council of European Union: Commission Implementing Regulation (EU) 2024/2493 of 23 September 2024 amending Implementing Regulation (EU) 2018/2066 as regards updating the monitoring and reporting of greenhouse gas emissions pursuant to Directive 2003/87/EC of the European Parliament and of the Council, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202402493 (last access: 17 July 2025), 2024. a
Driver, O. G. A., Stettler, M. E. J., and Gryspeerdt, E.: Factors limiting contrail detection in satellite imagery, Atmos. Meas. Tech., 18, 1115–1134, https://doi.org/10.5194/amt-18-1115-2025, 2025. a, b, c
DuBois, D. and Paynter, G. C.: ”Fuel Flow Method2” for Estimating Aircraft Emissions, SAE Transactions, 115, 1–14, https://doi.org/10.4271/2006-01-1987, 2006. a
Duda, D. P., Minnis, P., Nguyen, L., and Palikonda, R.: A Case Study of the Development of Contrail Clusters over the Great Lakes, J. Atmos. Sci., 61, 1132–1146, https://doi.org/10.1175/1520-0469(2004)061<1132:ACSOTD>2.0.CO;2, 2004. a
Garcia, M. A., Stafford, J., Minnix, J., and Dolan, J.: Aireon space based ADS-B performance model, in: 2015 Integrated Communication, Navigation and Surveillance Conference (ICNS), 21–23 April 2015, Herdon, VA, USA, C2-1–C2-10, https://doi.org/10.1109/ICNSURV.2015.7121219, 2015. a
Geraedts, S., Brand, E., Dean, T. R., Eastham, S., Elkin, C., Engberg, Z., Hager, U., Langmore, I., McCloskey, K., Yue-Hei Ng, J., Platt, J. C., Sankar, T., Sarna, A., Shapiro, M., and Goyal, N.: A scalable system to measure contrail formation on a per-flight basis, Environ. Res. Commun., 6, 015008, https://doi.org/10.1088/2515-7620/ad11ab, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, aa, ab, ac, ad
Gierens, K., Matthes, S., and Rohs, S.: How Well Can Persistent Contrails Be Predicted?, Aerospace, 7, 169, https://doi.org/10.3390/aerospace7120169, 2020. a, b, c
Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., and Sculley, D.: Google Vizier: A Service for Black-Box Optimization, in: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '17, Association for Computing Machinery, New York, NY, USA, 1487–1495, ISBN 9781450348874, https://doi.org/10.1145/3097983.3098043, 2017. a
Google: S2 Geometry, http://s2geometry.io/ (last access: 28 October 2024), 2024. a
Google: Contrails Explorer, https://contrails.webapps.google.com/ (last access: 17 July 2025), 2025. a
Gourgue, N., Boucher, O., and Barthès, L.: A dataset of annotated ground-based images for the development of contrail detection algorithms, Data in Brief, 59, 111364, https://doi.org/10.1016/j.dib.2025.111364, 2025. a
Gryspeerdt, E., Stettler, M. E. J., Teoh, R., Burkhardt, U., Delovski, T., Driver, O. G. A., and Painemal, D.: Operational differences lead to longer lifetimes of satellite detectable contrails from more fuel efficient aircraft, Environ. Res. Lett., 19, 084059, https://doi.org/10.1088/1748-9326/ad5b78, 2024. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a
Houchi, K., Stoffelen, A., Marseille, G. J., and De Kloe, J.: Comparison of wind and wind shear climatologies derived from high-resolution radiosondes and the ECMWF model, J. Geophys. Res.-Atmos., 115, D22123, https://doi.org/10.1029/2009JD013196, 2010. a
Jin, Z., Zhang, Y., Del Genio, A., Schmidt, G., and Kelley, M.: Cloud scattering impact on thermal radiative transfer and global longwave radiation, J. Quant. Spectrosc. Ra., 239, 106669, https://doi.org/10.1016/j.jqsrt.2019.106669, 2019. a
Kulik, L.: Satellite-based detection of contrails using deep learning, PhD thesis, Massachusetts Institute of Technology, https://hdl.handle.net/1721.1/124179, 2019. a
Lee, D., Fahey, D., Skowron, A., Allen, M., Burkhardt, U., Chen, Q., Doherty, S., Freeman, S., Forster, P., Fuglestvedt, J., Gettelman, A., De León, R., Lim, L., Lund, M., Millar, R., Owen, B., Penner, J., Pitari, G., Prather, M., Sausen, R., and Wilcox, L.: The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018, Atmos. Environ., 244, 117834, https://doi.org/10.1016/j.atmosenv.2020.117834, 2021. a, b
Low, J., Teoh, R., Ponsonby, J., Gryspeerdt, E., Shapiro, M., and Stettler, M. E. J.: Ground-based contrail observations: comparisons with reanalysis weather data and contrail model simulations, Atmos. Meas. Tech., 18, 37–56, https://doi.org/10.5194/amt-18-37-2025, 2025. a
Mannstein, H., Meyer, R., and Wendling, P.: Operational detection of contrails from NOAA-AVHRR-data, Int. J. Remote Sens., 20, 1641–1660, https://doi.org/10.1080/014311699212650, 1999. a, b
Mannstein, H., Spichtinger, P., and Gierens, K.: A note on how to avoid contrail cirrus, Transport. Res. D-Tr. E., 10, 421–426, https://doi.org/10.1016/j.trd.2005.04.012, 2005. a
Mannstein, H., Brömser, A., and Bugliaro, L.: Ground-based observations for the validation of contrails and cirrus detection in satellite imagery, Atmos. Meas. Tech., 3, 655–669, https://doi.org/10.5194/amt-3-655-2010, 2010. a
Martin Frias, A., Shapiro, M. L., Engberg, Z., Zopp, R., Soler, M., and Stettler, M. E. J.: Feasibility of contrail avoidance in a commercial flight planning system: an operational analysis, Environmental Research: Infrastructure and Sustainability, 4, 015013, https://doi.org/10.1088/2634-4505/ad310c, 2024. a, b
McCloskey, K., Geraedts, S., Jackman, B., Meijer, V. R., Brand, E., Fork, D., Platt, J. C., Elkin, C., and Van Arsdale, C.: A human-labeled Landsat-8 contrails dataset, in: Proceedings of the ICML 2021 Workshop on Tackling Climate Change with Machine Learning, virtually, 23 July 2020, vol. 23, 2021. a, b
Meerkötter, R., Schumann, U., Doelling, D., Minnis, P., Nakajima, T., and Tsushima, Y.: Radiative forcing by contrails, in: Annales Geophysicae, 17, 1080–1094, Springer, https://doi.org/10.1007/s00585-999-1080-7, 1999. a
Meijer, V. R., Kulik, L., Eastham, S. D., Allroggen, F., Speth, R. L., Karaman, S., and Barrett, S. R. H.: Contrail coverage over the United States before and during the COVID-19 pandemic, Environ. Res. Lett., 17, 034039, https://doi.org/10.1088/1748-9326/ac26f0, 2022. a, b
Meijer, V. R., Eastham, S. D., Waitz, I. A., and Barrett, S. R. H.: Contrail altitude estimation using GOES-16 ABI data and deep learning, Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, 2024. a, b, c
Minnis, P., Young, D. F., Garber, D. P., Nguyen, L., Smith Jr., W. L., and Palikonda, R.: Transformation of contrails into cirrus during SUCCESS, Geophys. Res. Lett., 25, 1157–1160, https://doi.org/10.1029/97GL03314, 1998. a
Ng, J. Y.-H., McCloskey, K., Cui, J., Meijer, V. R., Brand, E., Sarna, A., Goyal, N., Van Arsdale, C., and Geraedts, S.: Contrail Detection on GOES-16 ABI With the OpenContrails Dataset, IEEE T. Geosci. Remote, 62, 1–14, https://doi.org/10.1109/TGRS.2023.3345226, 2024. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w
National Oceanic and Atmospheric Administration (NOAA): GOES-16 Geostationary Operational Environmental Satellite data, NOAA [data set], https://console.cloud.google.com/storage/browser/gcp-public-data-goes-16, last access: 24 July 2025. a
Okuyama, A., Andou, A., Date, K., Hoasaka, K., Mori, N., Murata, H., Tabata, T., Takahashi, M., Yoshino, R., and Bessho, K.: Preliminary validation of Himawari-8/AHI navigation and calibration, in: Earth Observing Systems XX, edited by: Butler, J. J., Xiong, X. J., and Gu, X., International Society for Optics and Photonics, SPIE, 9607, 96072E, https://doi.org/10.1117/12.2188978, 2015. a
Petzold, A., Thouret, V., Gerbig, C., Zahn, A., Brenninkmeijer, C. A. M., Gallagher, M., Hermann, M., Pontaud, M., Ziereis, H., Boulanger, D., Marshall, J., Nédélec, P., Smit, H. G. J., Friess, U., Flaud, J.-M., Wahner, A., Cammas, J.-P., Volz-Thomas, A., and IAGOS Team: Global-scale atmosphere monitoring by in-service aircraft – current achievements and future prospects of the European Research Infrastructure IAGOS, Tellus B, 67, 28452, https://doi.org/10.3402/tellusb.v67.28452, 2015. a
Poll, D. and Schumann, U.: An estimation method for the fuel burn and other performance characteristics of civil transport aircraft in the cruise. Part 1 fundamental quantities and governing relations for a general atmosphere, Aeronaut. J., 125, 257–295, https://doi.org/10.1017/aer.2020.62, 2021. a
Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., and Willson, M.: Probabilistic weather forecasting with machine learning, Nature, 637, 84–90, https://doi.org/10.1038/s41586-024-08252-9, 2025. a
Sausen, R., Hofer, S., Gierens, K., Bugliaro, L., Ehrmanntraut, R., Sitova, I., Walczak, K., Burridge-Diesing, A., Bowman, M., and Miller, N.: Can we successfully avoid persistent contrails by small altitude adjustments of flights in the real world?, Meteorol. Z., 33, 83–98, https://doi.org/10.1127/metz/2023/1157, 2024. a, b, c, d
Schumann, U.: On conditions for contrail formation from aircraft exhausts, Meteorol. Z., 5, 4–23, https://doi.org/10.1127/metz/5/1996/4, 1996. a
Schumann, U., Mayer, B., Graf, K., and Mannstein, H.: A Parametric Radiative Forcing Model for Contrail Cirrus, J. Appl. Meteorol. Clim., 51, 1391–1406, https://doi.org/10.1175/JAMC-D-11-0242.1, 2012. a
Schumann, U., Hempel, R., Flentje, H., Garhammer, M., Graf, K., Kox, S., Lösslein, H., and Mayer, B.: Contrail study with ground-based cameras, Atmos. Meas. Tech., 6, 3597–3612, https://doi.org/10.5194/amt-6-3597-2013, 2013. a
Schumann, U., Penner, J. E., Chen, Y., Zhou, C., and Graf, K.: Dehydration effects from contrails in a coupled contrail–climate model, Atmos. Chem. Phys., 15, 11179–11199, https://doi.org/10.5194/acp-15-11179-2015, 2015. a
Shapiro, M., Engberg, Z., Teoh, R., Stettler, M., Dean, T., and Abbott, T.: pycontrails: Python library for modeling aviation climate impacts, Zenodo, https://doi.org/10.5281/zenodo.13357046, 2024. a, b
Teoh, R., Schumann, U., Majumdar, A., and Stettler, M. E. J.: Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption, Environ. Sci. Technol., 54, 2941–2950, https://doi.org/10.1021/acs.est.9b05608, 2020. a, b
Torr, P. H.: Geometric motion segmentation and model selection, Philos. T. Roy. Soc. A, 356, 1321–1340, https://doi.org/10.1098/rsta.1998.0224, 1998. a
Vazquez-Navarro, M., Mannstein, H., and Mayer, B.: An automatic contrail tracking algorithm, Atmos. Meas. Tech., 3, 1089–1101, https://doi.org/10.5194/amt-3-1089-2010, 2010. a
Vázquez-Navarro, M., Mannstein, H., and Kox, S.: Contrail life cycle and properties from 1 year of MSG/SEVIRI rapid-scan images, Atmos. Chem. Phys., 15, 8739–8749, https://doi.org/10.5194/acp-15-8739-2015, 2015. a
Voigt, C., Kleine, J., Sauer, D., Moore, R. H., Bräuer, T., Le Clercq, P., Kaufmann, S., Scheibe, M., Jurkat-Witschas, T., Aigner, M., Bauder, U., Boose, Y., Borrmann, S., Crosbie, E., Diskin, G. S., DiGangi, J., Hahn, V., Heckl, C., Huber, F., Nowak, J. B., Rapp, M., Rauch, B., Robinson, C., Schripp, T., Shook, M., Winstead, E., Ziemba, L., Schlager, H., and Anderson, B. E.: Cleaner burning aviation fuels can reduce contrail cloudiness, Commun. Earth Environ., 2, 114, https://doi.org/10.1038/s43247-021-00174-y, 2021. a
Zhang, C., Chen, L., Ding, S., Zhou, X., Chen, R., Zhang, X., Yu, Z., and Wang, J.: Mitigation effects of alternative aviation fuels on non-volatile particulate matter emissions from aircraft gas turbine engines: A review, Sci. Total Environ., 820, 153233, https://doi.org/10.1016/j.scitotenv.2022.153233, 2022. a
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...