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Persistent contrails make up a large fraction of aviation's contribution toglobal warming. We describe a scalable, automated detection and matching (ADM)system to determine from satellite data whether a flight has made a persistentcontrail. The ADM system compares flight segments to contrails detected by acomputer vision algorithm running on images from the GOES-16 Advanced BaselineImager. We develop a 'flight matching' algorithm and use it to label eachflight segment as a 'match' or 'non-match'. We perform this analysis on 1.6million flight segments. The result is an analysis of which flights makepersistent contrails several orders of magnitude larger than any previous work.We assess the agreement between our labels and available prediction modelsbased on weather forecasts. Shifting air traffic to avoid regions of contrailformation has been proposed as a possible mitigation with the potential forvery low cost/ton-CO2e. Our findings suggest that imperfections in theseprediction models increase this cost/ton by about an order of magnitude.Contrail avoidance is a cost-effective climate change mitigation even with thisfactor taken into account, but our results quantify the need for more accuratecontrail prediction methods and establish a benchmark for future development.

A scalable system to measure contrail formation on a per-flight basis