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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 35, issue 2
Ann. Geophys., 35, 203–215, 2017
https://doi.org/10.5194/angeo-35-203-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 35, 203–215, 2017
https://doi.org/10.5194/angeo-35-203-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Regular paper 06 Feb 2017

Regular paper | 06 Feb 2017

Three-dimensional data assimilation for ionospheric reference scenarios

Tatjana Gerzen1, Volker Wilken1, David Minkwitz1, Mainul M. Hoque1, and Stefan Schlüter2 Tatjana Gerzen et al.
  • 1German Aerospace Center (DLR), Institute of Communications and Navigation, Kalkhorstweg 53, 17235 Neustrelitz, Germany
  • 2European Space Agency ESA – EGNOS Project Office, 31401 Toulouse CEDEX 4, France

Abstract. The reliable estimation of ionospheric refraction effects is an important topic in the GNSS (Global Navigation Satellite Systems) positioning and navigation domain, especially in safety-of-life applications. This paper describes a three-dimensional ionosphere reconstruction approach that combines three data sources with an ionospheric background model: space- and ground-based total electron content (TEC) measurements and ionosonde observations. First the background model is adjusted by F2 layer characteristics, obtained from space-based ionospheric radio occultation (IRO) profiles and ionosonde data, and secondly the final electron density distribution is estimated by an algebraic reconstruction technique.

The method described is validated by TEC measurements of independent ground-based GNSS stations, space-based TEC from the Jason 1 and 2 satellites, and ionosonde observations. A significant improvement is achieved by the data assimilation, with a decrease in the residual errors by up to 98 % compared to the initial guess of the background. Furthermore, the results underpin the capability of space-based measurements to overcome data gaps in reconstruction areas where less GNSS ground-station infrastructure exists.

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