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))
BRUINSMA, Sean
(CNES)
04/02/2026, 15:00
MÜHLBAUER, Sebastian
(Federal Agency for Cartography and Geodesy)
04/02/2026, 15:15
BIAŁOBŁOCKI, Paweł
(ITTI Sp. o.o.)
04/02/2026, 15:30
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
ORMSTON, Thomas
(European Space Agency)
04/02/2026, 16:00