#27 AI for Climate-friendly Aircraft Trajectories (feat. Prof. Manuel Soler, Universidad Carlos III de Madrid)

Aerospace Ambition Podcast - Podcast tekijän mukaan Marius Wedemeyer - AAMBITION - Perjantaisin

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Get the latest insights from the AAMBITION Podcast delivered straight to your inbox. Subscribe HERE.++++Episode 27 of the “Aerospace Ambition Podcast” featuring Prof. Manuel Soler (Carlos III University of Madrid) is out!Talking Points• What are the goal and scope of E-CONTRAILS?• How can we address the delays in data availability from the Meteosat Third Generation satellite, and how will E-CONTRAILS handle this issue?• How can neural networks assist in assessing water vapor measurements? (Question from Dr Carmen Emmel)• Was the attention given to the topic of ground-based observation at the Pycontrails event justified?• Is the research community overly dependent on CoCiP?• Should the MRV place greater reliance on observational data?GuestManuel Soler is a Professor in the Department of Aerospace Engineering at UC3M in Madrid. He serves as the Director of the Doctoral Program in Aerospace Engineering, leads the UC3M Aeronautical Operations Laboratory, and co-founded the Spin-Off AI-Methods. His research focuses on mitigating the climate impact of aviation, particularly contrails. Manuel Soler has participated in numerous European projects related to contrails (e.g., FLYATM4E and ALARM, where he helped develop the ClimaCCF library). He is currently the coordinator of the E-CONTRAIL project, which aims to develop artificial neural networks (utilizing remote sensing detection methods) to predict the climate impact of contrails and aviation-induced cloudiness. This work contributes to a better understanding of the non-CO2 impact of aviation on global warming and helps reduce associated uncertainties, essential steps towards green aviation.Resources• Abolfazl Simorgh, Manuel Soler. Pathways to Sustainable Aviation: Aligning Flight Plans with Climate Goals, 03 June 2024, PREPRINT (Version 1) available at Research Square https://doi.org/10.21203/rs.3.rs-4355046/v1

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