Articles | Volume 17, issue 20
https://doi.org/10.5194/amt-17-6145-2024
© Author(s) 2024. 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-17-6145-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2024
Research article |
| 23 Oct 2024
| 23 Oct 2024
Contrail altitude estimation using GOES-16 ABI data and deep learning
Vincent R. Meijer
CORRESPONDING AUTHOR
Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: Faculty of Aerospace Engineering, Delft University of Technology, Delft, the Netherlands
Sebastian D. Eastham
Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USA
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: Brahmal Vasudevan Institute for Sustainable Aviation, Department of Aeronautics, Imperial College London, London, UK
Ian A. Waitz
Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USA
Steven R. H. Barrett
Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, MA, USA
now at: Department of Engineering, University of Cambridge, Cambridge, UK
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Inés Sanz-Morère, Sebastian D. Eastham, Florian Allroggen, Raymond L. Speth, and Steven R. H. Barrett
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Contrails cause ~50 % of aviation climate impacts, but this is highly uncertain. This is partly due to the effect of overlap between contrails and other cloud layers. We developed a model to quantify this effect, finding that overlap with natural clouds increased contrails' radiative forcing in 2015. This suggests that cloud avoidance may help in reducing aviation's climate impacts. We also find that contrail–contrail overlap reduces impacts by ~3 %, increasing non-linearly with optical depth.
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
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
Burkhardt, U., Bock, L., and Bier, A.: Mitigating the contrail cirrus climate impact by reducing aircraft soot number emissions, npj Climate and Atmospheric Science, 1, 37, https://doi.org/10.1038/s41612-018-0046-4, 2018. a
Champion, K. S. W., Cole, A. E., and Kantor, A. J.: Standard and reference atmospheres, Handbook of geophysics and the space environment, Citeseer, 14, 1985. a
Chang, F.-L., Minnis, P., Lin, B., Khaiyer, M. M., Palikonda, R., and Spangenberg, D. A.: A modified method for inferring upper troposphere cloud top height using the GOES 12 imager 10.7 and 13.3 µm data, J. Geophys. Res.-Atmos., 115, D06208, https://doi.org/10.1029/2009JD012304, 2010. a
Chevallier, R., Shapiro, M., Engberg, Z., Soler, M., and Delahaye, D.: Linear Contrails Detection, Tracking and Matching with Aircraft Using Geostationary Satellite and Air Traffic Data, Aerospace, 10, 578, https://doi.org/10.3390/aerospace10070578, 2023. a, b
Dirksen, R. J., Sommer, M., Immler, F. J., Hurst, D. F., Kivi, R., and Vömel, H.: Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde, Atmos. Meas. Tech., 7, 4463–4490, https://doi.org/10.5194/amt-7-4463-2014, 2014. a
Fehlberg, E.: Klassische Runge-Kutta-Formeln vierter und niedrigerer Ordnung mit Schrittweiten-Kontrolle und ihre Anwendung auf Wärmeleitungsprobleme, Computing, 6, 61–71, https://doi.org/10.1007/BF02241732, 1970. a
Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Alken, P., Booth, M., Rossi, F., and Ulerich, R.: GNU scientific library, Network Theory Limited Godalming, https://www.k0d.cc/storage/books/C/GNU%20Scientific%20Library%202.6.pdf (last access: 20 February 2024), 2002. a
Garnier, A., Trémas, T., Pelon, J., Lee, K.-P., Nobileau, D., Gross-Colzy, L., Pascal, N., Ferrage, P., and Scott, N. A.: CALIPSO IIR Version 2 Level 1b calibrated radiances: analysis and reduction of residual biases in the Northern Hemisphere, Atmos. Meas. Tech., 11, 2485–2500, https://doi.org/10.5194/amt-11-2485-2018, 2018. a
Geraedts, S., Brand, E., Dean, T., Eastham, S. D., Elkin, C., Engberg, Z., Hager, U., Langmore, I., McCloskey, K., and Ng, J. Y.-H.: A scalable system to measure contrail formation on a per-flight basis, Environmental Research Communications, 6, 015008, https://doi.org/10.1088/2515-7620/ad11ab, 2023. a, b
Gierens, K. and Spichtinger, P.: On the size distribution of ice-supersaturated regions in the upper troposphere and lowermost stratosphere, Ann. Geophys., 18, 499–504, https://doi.org/10.1007/s00585-000-0499-7, 2000. a
Gierens, K., Matthes, S., and Rohs, S.: How Well Can Persistent Contrails Be Predicted?, Aerospace, 7, 169, https://doi.org/10.3390/aerospace7120169, 2020a. a
Gierens, K., Wilhelm, L., Sommer, M., and Weaver, D.: On ice supersaturation over the Arctic, Meteorol. Z., 29, 165–176, https://doi.org/10.1127/metz/2020/1012, 2020b. a, b
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, Adaptive computation and machine learning, The MIT Press, Cambridge, Massachusetts, ISBN 978-0-262-03561-3, 2016. a
Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P. N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J. F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., and Wind, G.: Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms, Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, 2014. a
Heidinger, A. K., Pavolonis, M. J., Calvert, C., Hoffman, J., Nebuda, S., Straka, W., Walther, A., and Wanzong, S.: Chapter 6 – ABI Cloud Products from the GOES-R Series, in: The GOES-R Series, edited by: Goodman, S. J., Schmit, T. J., Daniels, J., and Redmon, R. J., Elsevier, 43–62, https://doi.org/10.1016/B978-0-12-814327-8.00006-8, ISBN 978-0-12-814327-8, 2020. a, b
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., 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., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Heymsfield, A., Baumgardner, D., DeMott, P., Forster, P., Gierens, K., and Kärcher, B.: Contrail microphysics, B. Am. Meteorol. Soc., 91, 465–472, 2010. a
Hyland, R. W. and Hexler, A.: Formulations for the thermodynamic properties of the saturated phases of H2O from 173.15 to 473.15 K, ASHRAE Tran., 89, 500–519, 1983. a
Iwabuchi, H., Yang, P., Liou, K., and Minnis, P.: Physical and optical properties of persistent contrails: Climatology and interpretation, J. Geophys. Res.-Atmos., 117, D06215, https://doi.org/10.1029/2011JD017020, 2012. a, b
Kalluri, S., Alcala, C., Carr, J., Griffith, P., Lebair, W., Lindsey, D., Race, R., Wu, X., and Zierk, S.: From Photons to Pixels: Processing Data from the Advanced Baseline Imager, Remote Sensing, 10, 177, https://doi.org/10.3390/rs10020177, 2018. a
Kärcher, B.: Formation and radiative forcing of contrail cirrus, Nat. Commun., 9, 1824, https://doi.org/10.1038/s41467-018-04068-0, 2018. a, b
Kuma, P.: Visualising Data from CloudSat and CALIPSO Satellites, PhD thesis, Zenodo, https://doi.org/10.5281/zenodo.3764234, 2010. a
Lamquin, N., Stubenrauch, C. J., Gierens, K., Burkhardt, U., and Smit, H.: A global climatology of upper-tropospheric ice supersaturation occurrence inferred from the Atmospheric Infrared Sounder calibrated by MOZAIC, Atmos. Chem. Phys., 12, 381–405, https://doi.org/10.5194/acp-12-381-2012, 2012. 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. R., Lim, L. L., Lund, M. T., Millar, R. J., Owen, B., Penner, J. E., Pitari, G., Prather, M. J., Sausen, R., and Wilcox, L. J.: 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, 2020. a
Liou, K. N.: An Introduction to Atmospheric Radiation, Elsevier, ISBN 978-0-08-049167-7, 2002. a
Meijer, V. R., Eastham, S. D., Barrett, S. R., and Waitz, I. A.: Contrail altitude estimation using GOES-16 ABI data and deep learning: Dataset of contrails collocated with CALIOP satellite measurements, Zenodo [data set], https://doi.org/10.5281/zenodo.13737958, 2024a. a
Meijer, V., Barrett, S., Eastham, S., and Waitz, I.: Contrail altitude estimation using GOES-16 ABI data and deep learning: software, Zenodo [data set], https://doi.org/10.5281/zenodo.13959874, 2024b. a
Ng, J. Y.-H., McCloskey, K., Cui, J., Meijer, V. R., Brand, E., Sarna, A., Goyal, N., Van Arsdale, C., and Geraedts, S.: OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.02122, 20 April 2023. a
Petzold, A., Neis, P., Rütimann, M., Rohs, S., Berkes, F., Smit, H. G. J., Krämer, M., Spelten, N., Spichtinger, P., Nédélec, P., and Wahner, A.: Ice-supersaturated air masses in the northern mid-latitudes from regular in situ observations by passenger aircraft: vertical distribution, seasonality and tropospheric fingerprint, Atmos. Chem. Phys., 20, 8157–8179, https://doi.org/10.5194/acp-20-8157-2020, 2020. a, b
Pfreundschuh, S., Eriksson, P., Duncan, D., Rydberg, B., Håkansson, N., and Thoss, A.: A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems, Atmos. Meas. Tech., 11, 4627–4643, https://doi.org/10.5194/amt-11-4627-2018, 2018. a, b
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P.: Numerical Recipes: The Art of Scientific Computing, 3rd edn., Cambridge University Press, ISBN 9780521880688, 2007. 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, 2023. a, b
Schmetz, J., Holmlund, K., Hoffman, J., Strauss, B., Mason, B., Gaertner, V., Koch, A., and Berg, L. V. D.: Operational Cloud-Motion Winds from Meteosat Infrared Images, J. Appl. Meteorol. Clim., 32, 1206–1225, https://doi.org/10.1175/1520-0450(1993)032<1206:OCMWFM>2.0.CO;2, 1993. a
Schmidt, E.: Die entstehung von eisnebel aus den auspuffgasen von flugmotoren, Schriften der Deutschen Akademie der Luftfahrtforschung, Verlag R. Oldenbourg, München, Heft 44, 5, 1–15, 1941. a
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., Baumann, R., Baumgardner, D., Bedka, S. T., Duda, D. P., Freudenthaler, V., Gayet, J.-F., Heymsfield, A. J., Minnis, P., Quante, M., Raschke, E., Schlager, H., Vázquez-Navarro, M., Voigt, C., and Wang, Z.: Properties of individual contrails: a compilation of observations and some comparisons, Atmos. Chem. Phys., 17, 403–438, https://doi.org/10.5194/acp-17-403-2017, 2017. a
Schumann, U., Bugliaro, L., Dörnbrack, A., Baumann, R., and Voigt, C.: Aviation Contrail Cirrus and Radiative Forcing Over Europe During 6 Months of COVID‐19, Geophys. Res. Lett., 48, e2021GL092771, https://doi.org/10.1029/2021GL092771, 2021. a
Schäfer, M., Strohmeier, M., Lenders, V., Martinovic, I., and Wilhelm, M.: Bringing up OpenSky: A large-scale ADS-B sensor network for research, in: IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, Berlin, Germany, 15–17 April 2014, IEEE, 83–94, https://doi.org/10.1109/IPSN.2014.6846743, 2014. a
Smith, W. L., Woolf, H. M., and Jacob, W. J.: A regression method for obtaining real-time temperature and geopotential height profiles from satellite spectrometer measurements and its application to Nimbus 3 “SIRS” observations, Mon. Weather Rev., 98, 582–603, 1970. a
Sommer, M., Dirksen, R., and Von Rohden, C.: Brief description of the RS92 GRUAN data product (RS92-GDP), GRUAN Lead Centre, Lindenberg, Germany, 2016. a
Spichtinger, P., Gierens, K., and Read, W.: The global distribution of ice-supersaturated regions as seen by the Microwave Limb Sounder, Q. J. Roy. Meteor. Soc., 129, 3391–3410, 2003b. a
Strandgren, J., Fricker, J., and Bugliaro, L.: Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI, Atmos. Meas. Tech., 10, 4317–4339, https://doi.org/10.5194/amt-10-4317-2017, 2017b. 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
Teoh, R., Schumann, U., Voigt, C., Schripp, T., Shapiro, M., Engberg, Z., Molloy, J., Koudis, G., and Stettler, M. E. J.: Targeted Use of Sustainable Aviation Fuel to Maximize Climate Benefits, Environ. Sci. Technol., 56, 17246–17255, https://doi.org/10.1021/acs.est.2c05781, 2022. a
Treffeisen, R., Krejci, R., Ström, J., Engvall, A. C., Herber, A., and Thomason, L.: Humidity observations in the Arctic troposphere over Ny-Ålesund, Svalbard based on 15 years of radiosonde data, Atmos. Chem. Phys., 7, 2721–2732, https://doi.org/10.5194/acp-7-2721-2007, 2007. a, b
Vicente, G., Davenport, J., and Scofield, R.: The role of orographic and parallax corrections on real time high resolution satellite rainfall rate distribution, Int. J. Remote Sens., 23, 221–230, 2002. 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, Bruce E.: Cleaner burning aviation fuels can reduce contrail cloudiness, Communications Earth & Environment, 2, 114, https://doi.org/10.1038/s43247-021-00174-y, 2021. 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
Wang, Z., Bugliaro, L., Jurkat-Witschas, T., Heller, R., Burkhardt, U., Ziereis, H., Dekoutsidis, G., Wirth, M., Groß, S., Kirschler, S., Kaufmann, S., and Voigt, C.: Observations of microphysical properties and radiative effects of a contrail cirrus outbreak over the North Atlantic, Atmos. Chem. Phys., 23, 1941–1961, https://doi.org/10.5194/acp-23-1941-2023, 2023. a
Welch, B. L.: The generalization of ‘student's’ problem when several different population variances are involved, Biometrika, 34, 28–35, https://doi.org/10.1093/biomet/34.1-2.28, 1947. a
Wilks, D. S.: Statistical methods in the atmospheric sciences, Vol. 100, Academic Press, ISBN-13 978-0123850225, 2011. a
Winker, D., Pelon, J., Coakley Jr., J., Ackerman, S., Charlson, R., Colarco, P., Flamant, P., Fu, Q., Hoff, R., Kittaka, C., Kubar, T. L., Le Treut, H., Mccormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO mission: A global 3D view of aerosols and clouds, B. Am. Meteorol. Soc., 91, 1211–1230, 2010. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
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.
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and...
