Description
This session examines how new data and updated modelling techniques can improve satellite drag estimation, thermospheric density predictions, and overall orbit forecasting. Using current space-weather data alongside modern analytical methods, as well as state-of-the-art AI forecasting techniques, the discussion focuses on how reducing uncertainty in drag predictions can support more informed operational decisions, ultimately helping to limit unnecessary collision-avoidance manoeuvres and improve reliability in an increasingly crowded space environment.
(Convenors: Myrto Tzamali (ESA), Philippe Yaya (CNES/CLS))
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BRUINSMA, Sean (CNES)04/02/2026, 15:00
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MÜHLBAUER, Sebastian (Federal Agency for Cartography and Geodesy)04/02/2026, 15:15
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BIAŁOBŁOCKI, Paweł (ITTI Sp. o.o.)04/02/2026, 15:30
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KRAUSS, Sandro (Graz University of Technology), STRASSER, Andreas (Graz University of Technology, Institute for Geodesy), TEMMER, Manuela (University of Graz), MILOSIC, Daniel (University of Graz)04/02/2026, 15:45
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ORMSTON, Thomas (European Space Agency)04/02/2026, 16:00
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04/02/2026, 16:15