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Artificial Neural Networks for the Prediction of Contrails and Aviation Induced Cloudiness

CORDIS · observation · Publication date unknown

o 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) de existing gap in terms of understanding aviation-induced climate impact. The overall purpose of E-CONTRAIL project 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. Specifically, the objectives of E-CONTRAIL are: O-1 to develop remote sensing algorithms for the detection of contrails and aviation-induced cloudiness. O-2 to quantify the radiative forcing of ice clouds based on remote sensing and radiative transfer methods. O-3 to use of deep learning arc

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recordType

award

status

SIGNED

region

EU

value

997500

unit

EUR