Project description
Estimating non-CO2 environmental damage from aviation
Aviation is known to have a negative impact on the climate due to harmful emissions. However, recent studies and research have uncovered additional effects, particularly concerning the role of contrails and anthropogenic cirrus in cooling the atmosphere and causing cloudiness. Unfortunately, research on this topic has been limited due to the complexity of various variables involved. The EU-funded E-CONTRAIL project aims to conduct a comprehensive study on contrails and aviation-induced cloudiness leveraging state-of-the-art satellite imaginary, exploring their potential impact on the environment. Additionally, the project seeks to develop an artificial neural network capable of predicting non-CO2 aviation damage to the environment. To achieve this goal, the researchers will draw upon extensive knowledge in deep learning and climate science to minimise uncertainties.
Objective
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) 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 architectures to generate AI models capable of predicting the radiative forcing of contrails based on data-archive numerical weather forecasts and historical traffic
O-4 to assess the climate impact and develop a visualization tool in a dashboard
Upon successful achievement of the objectives described above, we ambition to provide aviation stakeholders with an early and accurate (thus, reducing the associated uncertainty) prediction of those volumes of airspace with the conditions for large global warming impact due to contrails and aviation-induced cloudiness.
Fields of science
- natural sciencesearth and related environmental sciencesatmospheric sciencesmeteorology
- engineering and technologyenvironmental engineeringremote sensing
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesearth and related environmental sciencesatmospheric sciencesclimatologyclimatic changes
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Programme(s)
- HORIZON.2.5 - Climate, Energy and Mobility Main Programme
Topic(s)
Funding Scheme
HORIZON-JU-RIA - HORIZON JU Research and Innovation ActionsCoordinator
28903 Getafe (Madrid)
Spain