The E-CONTRAIL project received the acceptance of a conference paper to be presented at the 4th ECATS International Conference on "Making aviation environmentally sustainable". The conference will take place in Delft, the Netherlands from the 24th until the 26th of October 2023.
The paper is called "New method of contrails detection using geostationary imagers" and has been produced by Hugues Brenot1, Manuel Soler2, Pierre de Buyl3, Ricardo Vinuesa4 and the E-CONTRAIL team.
(1) Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium.
(2) University Carlos III of Madrid (UC3M), Spain.
(3) Royal Meteorological Institute of Belgium (KMI-IRM), Brussels, Belgium.
(4) Royal Institute of Technology (KTH), Stockholm, Sweden.
ABSTRACT
Non-CO2 emissions, contrails can form and alter the radiation budget (radiative forcing). The overall purpose of the E-CONTRAIL SESAR JU project (www.econtrail.com) 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. One of E-CONTRAIL’s ambitions is to bring together state of the art multispectral geostationary imagery and radiative transfer models for an accurate quantification of the ice clouds radiative forcing all along the diurnal cycle. The first step is to retrieve high altitude ice crystal detection using radiances and brightness temperature from geostationary imagers. This presentation will focus on showing a new method to detect contrails using data from SEVIRI geostationary multi-spectral sensor onboard Meteosat Second Generation. To isolate the signature of ice crystal, the presented method uses a Covariance-Based Retrieval Algorithm, so called COBRA. In a second step, the proper detection of contrails requires to synchronise ice observations with aerial traffic (to discriminate clouds induced by civil aviation from those that are natural or are induced by other sources different from civil aviation, e.g., the military).
To carry out this discrimination, we will develop a contrail and aviation-induced cloudiness tracking algorithm, including the temporal and spatial synchronisation of contrail data with traffic data to associate each detected contrail to the correspondent flight and the determination of the operational characteristics of each flight in terms of aircraft characteristics, flight profile, and altitude.
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