Solar radio proxies for improved satellite orbit prediction

Philippe Yaya 1 Louis Hecker 2 Thierry Dudok de Wit 3 Clémence Le Fèvre 4 Sean Bruinsma 4
1 CLS - Collecte Localisation Satellites
2 IPGS - Institut de physique du globe de Strasbourg
3 LPC2E - Laboratoire de Physique et Chimie de l'Environnement et de l'Espace
4 CNES - Centre National d'Etudes Spatiales
Abstract : – Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.
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Philippe Yaya, Louis Hecker, Thierry Dudok de Wit, Clémence Le Fèvre, Sean Bruinsma. Solar radio proxies for improved satellite orbit prediction. Journal of Space Weather and Space Climate, EDP sciences, 2017, 7 (17 pages), ⟨10.1051/swsc/2017032⟩. ⟨insu-01691849⟩

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