Articles | Volume 19, issue 21
https://doi.org/10.5194/amt-19-6891-2026
https://doi.org/10.5194/amt-19-6891-2026
Research article
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30 Oct 2026
Research article | Highlight paper |  | 30 Oct 2026

Observing formation and early evolution of contrails formed by IAGOS aircraft using high-resolution LEO satellite imagery

Thymen Woldhuis, Zebediah Engberg, Susanne Rohs, and Vincent Meijer

Data sets

Dataset accompanying "Observing formation and early evolution of contrails formed by IAGOS aircraft using high-resolution LEO satellite imagery" Thymen Woldhuis et al. https://doi.org/10.4121/2d66d65e-8041-4435-ab3c-0af3fdfc5d23

Model code and software

Code accompanying "Observing formation and early evolution of contrails formed by IAGOS aircraft using high-resolution LEO satellite imagery" Thymen Woldhuis et al. https://doi.org/10.4121/a1e4d5b4-5af2-4a13-8152-a1e8d49c297f

pycontrails: Python library for modeling aviation climate impacts (Version v0.60.2) Marc Shapiro et al. https://doi.org/10.5281/zenodo.18260617

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Editorial statement
Aircraft contrails can persist for hours and spread into contrail cirrus, thereby contributing to aviation's climate impact. Reducing this impact through aircraft rerouting requires skillful predictions of contrail formation, persistence, and impact. Evaluating these predictions is difficult because discrepancies with observations can arise from uncertain meteorological inputs, observational errors, and the limitations of contrail models themselves. This work locates aircraft carrying in-situ instruments in high-resolution satellite imagery to observe contrail formation and early evolution. By combining these observations with meteorological measurements from the aircraft, the resulting dataset is used to evaluate contrail models while reducing uncertainty associated with meteorological inputs and observational constraints.
Short summary
We introduce an algorithm that can find individual aircraft in high-resolution (10–30 m) satellite imagery to see whether they form contrails. We looked specifically for IAGOS (In-service Aircraft for a Global Observing System) aircraft which measure humidity: a key variable in predicting contrail formation and evolution. The final dataset consists of 543 IAGOS flights for which we know whether contrails form or not. We find excellent (98.3 %) agreement between these observations and predictions by a thermodynamic model of contrail formation.
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