OUR GOALS
Artificial Neural Networks for the Prediction of Contrails and Aviation Induced Cloudiness
Contrails and aviation-induced cloudiness effects on climate change show large uncertainties since they are subject to meteorological, regional, and seasonal variations. Indeed, under some specific circumstances, aircraft can generate anthropogenic cirrus with cooling. Thus, the need for research into contrails and aviation-induced cloudiness and its associated uncertainties to be considered in aviation climate mitigation actions becomes unquestionable. We will blend cutting-edge AI techniques (deep learning) and climate science with application to the aviation domain, aiming at closing (at least partially) the existing gap in terms of understanding aviation-induced climate impact.
The overall purpose is to develop artificial neural networks (leveraging remote sensing detection methods) for the prediction of the climate impact derived from contrails and aviation-induced cloudiness, contributing, thus, to a better understanding of the non-CO2 impact of aviation on global warming and reducing their associated uncertainties as essential steps towards green aviation.
E-Contrail Project in a Nutshell
We ambition to blend cutting-edge AI techniques, namely deep learning, and climate science with application to the aviation domain, aiming at closing (at least partially) the existing gap in terms of understanding aviation-induced climate impact. Indeed, the possibility of using Artificial Intelligence (AI) techniques is emerging with great force in various fields. In a recent survey published in Nature communications by Vinuesa et al., the authors claim that AI can enable the accomplishment of 134 targets across all the goals established in 2030 Agenda for Sustainable Development. This includes Earth sciences (climate change and meteorological prediction) and the aviation domain.
Related projects
- E-CONTRAIL↗ Artificial Neural Networks for the Prediction of Contrails and Aviation Induced Cloudiness
Highlighted publications
- Rémi Chevallier, Marc Shapiro, Zebediah Engberg, Manuel Soler, Daniel Delahaye. Condensation Trails Detection, Tracking and Matching with Aircraft using Geostationary Satellite and Air Traffic data. Aerospace, 10(7), 578; https://doi.org/10.3390/aerospace10070578