Climate impact models of the non-${\mathrm{CO}}_2$ emissions of aviation are still subject to significant uncertainties. Condensation trails, or contrails, are one of these non-${\mathrm{CO}}_2$ 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.

Condensation trail (contrail) cirrus clouds cause a substantial fraction of aviation's climate impact. One proposed method for the mitigation of this impact involves modifying flight paths to avoid particular regions of the atmosphere that are conducive to the formation of persistent contrails, which can transform into contrail cirrus. Determining the success of such avoidance maneuvers can be achieved by ascertaining which flight formed each nearby contrail observed in satellite imagery. The same process can be used to assess the skill of contrail forecast models. The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities, and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but it lacks an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail detections with predetermined contrail-to-flight attributions that can be used to evaluate – or “benchmark” – and improve such attribution algorithms. The resulting performance metrics can be employed to understand the implications of using these observational data in downstream tasks, such as forecast model evaluation and the analysis of contrail avoidance trials, although the metrics do not directly quantify real-world performance. We also introduce a novel, highly scalable contrail-to-flight attribution algorithm that leverages the characteristic compounding of error induced by simulating contrail advection using numerical weather models. The benchmark shows an improvement of approximately 25 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.

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Contrail cirrus clouds persisting in ice-supersaturated air cause a substantial fraction of aviation's climate impact. One proposed method for the mitigation of this impact involves modifying flight paths to avoid particular regions of the atmosphere that are conducive to the formation of persistent contrails. Ascertaining which flight formed each observed contrail can be used to assess and improve contrail forecast models, as well as study the effectiveness of performing contrail avoidance. The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but lack an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail observations with predetermined contrail-to-flight attributions which can be used to evaluate – or "benchmark" – and improve such attribution algorithms. The resulting performance metrics can be used to understand the implications of using this observational data in downstream tasks such as forecast model evaluation and analysis of contrail avoidance trials. We also introduce a novel, highly-scalable, contrail-to-flight attribution algorithm that leverages the characteristic compounding of error induced by simulating contrail advection using numerical weather models. The benchmark shows an improvement of about 30 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.

Contrail cirrus clouds persisting in ice-supersaturated air cause a substantial fraction of aviation's climate impact. One proposed method for the mitigation of this impact involves modifying flight paths to avoid particular regions of the atmosphere that are conducive to the formation of persistent contrails. Ascertaining which flight formed each observed contrail can be used to assess and improve contrail forecast models, as well as study the effectiveness of performing contrail avoidance. The problem of contrail-to-flight attribution is complicated by several factors, such as the time required for a contrail to become visible in satellite imagery, high air traffic densities and errors in wind data. Recent work has introduced automated algorithms for solving the attribution problem, but lack an evaluation against ground-truth data. In this work, we present a method for producing synthetic contrail observations with predetermined contrail-to-flight attributions which can be used to evaluate – or "benchmark" – and improve such attribution algorithms. The resulting performance metrics can be used to understand the implications of using this observational data in downstream tasks such as forecast model evaluation and analysis of contrail avoidance trials. We also introduce a novel, highly-scalable, contrail-to-flight attribution algorithm that leverages the characteristic compounding of error induced by simulating contrail advection using numerical weather models. The benchmark shows an improvement of about 30 % in precision versus previous contrail-to-flight attribution algorithms, without compromising recall.