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Linear contrails detection, tracking and matching with aircraft using geostationary satellite and air traffic data

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Ingeniería Aeroespaciales
dc.contributor.authorChevallier, R.
dc.contributor.authorShapiro, M.
dc.contributor.authorEngberg, Z.
dc.contributor.authorSoler Arnedo, Manuel Fernando
dc.contributor.authorDelahaye, D.
dc.date.accessioned2024-01-22T17:33:04Z
dc.date.available2024-01-22T17:33:04Z
dc.date.issued2023-07-01
dc.description.abstractClimate impact models of the non- (Formula presented.) emissions of aviation are still subject to significant uncertainties. Condensation trails, or contrails, are one of these non- (Formula presented.) effects. In order to validate the contrail simulation models, a dataset of observations covering the entire lifetime of the contrails will be required, as well as the characteristics of the aircraft which produced them. This study carries on the work on contrail observation from geostationary satellite by proposing a new way to track contrails and identify the flight that produced it using geostationary satellite infrared images, weather data as well as air traffic data. It solves the tracking and the identification problem as one, each process leveraging information from the other to achieve a better overall result. This study is a new step towards a consistent contrail dataset that could be used to validate contrail models.en
dc.description.sponsorshipWe would like to thank Airbus Airline Sciences team for their funding and support, B. Sridhar for the insightful discussion and suggestions, MIT LAE team for their help on the contrail detection topic, and Enac Optim team for the reviews and support.en
dc.description.statusPublicadoes
dc.format.extent27
dc.identifier.bibliographicCitationChevallier, R.; Shapiro, M.; Engberg, Z.; Soler, M.; Delahaye, D. Linear Contrails Detection, Tracking and Matching with Aircraft Using Geostationary Satellite and Air Traffic Data. Aerospace 2023, 10, 578 (36 p.). https://doi.org/10.3390/aerospace10070578en
dc.identifier.doihttps://doi.org/10.3390/aerospace10070578
dc.identifier.issn2226-4310
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue7, 578
dc.identifier.publicationlastpage27
dc.identifier.publicationtitleAerospaceen
dc.identifier.publicationvolume10
dc.identifier.urihttps://hdl.handle.net/10016/39421
dc.identifier.uxxiAR/0000033697
dc.language.isoengen
dc.publisherMDPIes
dc.rights© 2023 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaAeronáuticaes
dc.subject.ecienciaFísicaes
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.otherCondensation Trailsen
dc.subject.otherContrail Trackingen
dc.subject.otherImage Segmentationen
dc.subject.otherInstance Segmentationen
dc.subject.otherMatching Contrails With Aircraften
dc.subject.otherNon-Co2 Climate Impact Of Aviationen
dc.subject.otherSatellite Imageryen
dc.titleLinear contrails detection, tracking and matching with aircraft using geostationary satellite and air traffic dataen
dc.typeresearch articleen
dc.type.hasVersionVoRen
dspace.entity.typePublication
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