Satellite-based detection of contrails using deep learning

Aircraft condensation trails, known as contrails, are estimated to account for approximately half of all climate warming resulting from aviation emissions, but uncertainty in coverage is high [12, 2]. These artificial clouds form behind aircraft and trap outgoing infrared radiation, leading to a warming effect [12, 3, 28, 40, 3]. With aviation forecast to contribute up to 15% of total anthropogenic warming by 2050 [18], an accurate estimate of total contrail-attributable warming is required. Automated detection of contrails using satellite images has not been feasible to date due to the complex evolution of contrail shapes and their similarity to cirrus clouds and land features [21, 12, 39]. This has been a limiting factor in quantifying contrail climate impacts [39, 3]. We use a deep learning approach to overcome these difficulties.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Kulik, Luke.

Advisor dc:contributor.advisor

• Steven R.H. Barrett.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124179

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124179