Articles | Volume 17, issue 20
https://doi.org/10.5194/amt-17-6145-2024
https://doi.org/10.5194/amt-17-6145-2024
Research article
 | 
23 Oct 2024
Research article |  | 23 Oct 2024

Contrail altitude estimation using GOES-16 ABI data and deep learning

Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett

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Cited articles

Agarwal, A., Meijer, V. R., Eastham, S. D., Speth, R. L., and Barrett, S. R.: 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
Amell, A., Eriksson, P., and Pfreundschuh, S.: Ice water path retrievals from Meteosat-9 using quantile regression neural networks, Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, 2022. a, b, c, d, e
Appleman, H.: The formation of exhaust condensation trails by jet aircraft, B. Am. Meteorol. Soc., 34, 14–20, 1953. a
Bieliński, T.: A parallax shift effect correction based on cloud height for geostationary satellites and radar observations, Remote Sensing, 12, 365, https://doi.org/10.3390/rs12030365, 2020. a
Braun, B. M., Sweetser, T. H., Graham, C., and Bartsch, J.: CloudSat's A-Train Exit and the Formation of the C-Train: An Orbital Dynamics Perspective, in: 2019 IEEE Aerospace Conference, Big Sky, MT, USA, 2–9 March 2019, IEEE, 1–10, https://doi.org/10.1109/AERO.2019.8741958, ISSN: 1095-323X, 2019. a
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Short summary
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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