A manually labeled contrail dataset from MSG/SEVIRI
Vanessa Santos Gabriel (University of Mainz)
Luca Bugliaro (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Mara Montag (University of Mainz)
Sabrina Ries (University of Mainz)
Ziming Wang (University of Mainz)
Kai Widmaier (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Matteo Arico (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Elena de la Torre Castro (Deutsches Zentrum für Luft- und Raumfahrt (DLR), GE Aviation)
Liam Megill (Deutsches Zentrum für Luft- und Raumfahrt (DLR), TU Delft - Aerospace Engineering)
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Abstract
Contrails – thin ice clouds formed by aircraft – are a major contributor to aviation-induced climate forcing, yet their observational characterization remains limited. We present a manually labeled contrail dataset derived from observations of the Meteosat Second Generation (MSG) SEVIRI instrument over Europe and the North Atlantic, comprising 140 scenes of 256 × 256 pixels at 3 km nominal resolution. The dataset covers the time period in which Meteosat-10 was the operational satellite (from January 2013 through February 2018 and from March 2023 through March 2024) and scenes are distributed randomly over the whole SEVIRI disk. Each scene was independently annotated by three labelers, with ground truth established via majority consensus. To provide additional context, the dataset includes outputs from two satellite retrievals: CiPS (Cirrus Properties from SEVIRI) and ProPS (Probabilistic Cloud Top Phase retrieval), offering information on cloud cover and cloud top phase, cirrus probability, ice optical thickness, and ice cloud top height. These complementary variables enable detailed investigations, such as factors influencing contrail visibility. The dataset supports analyses of contrail detection, contrail characteristics, cloud-contrail interactions, and environmental conditions affecting detection. By providing high-quality labeled data with auxiliary cloud information, this resource facilitates the development and evaluation of contrail studies, contributes to improved understanding of aviation-related cloud effects, and informs strategies for climate impact mitigation.
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