Describing Temporal Variations of the Geomagnetic Field through a Modified Virtual Observatory Scheme: Application to SWARM Measurements

Friday, 19 December 2014
Diana Saturnino1, Benoit Langlais1, Hagay Amit1 and Mioara Mandea2, (1)LPGN Laboratoire de Planétologie et Géodynamique de Nantes, Nantes Cedex 03, France, (2)CNES - Centre National d'Etudes Spatiales, Paris, France
We propose a new approach to describe the spatial variability of the temporal changes of the geomagnetic field using spacecraft measurements. Temporal changes are well described at a given location by observatory time series.
A study on its spatial variability is hampered by the uneven distribution of the observatories. The global coverage of magnetic satellite data has led to drastically improved geomagnetic field models. However it is both difficult
to directly compare satellite and observatory data due to satellite movements, and to describe the secular variation (SV) in situ as it is done at ground observatory locations. To overcome this we follow a Virtual Observatories (VO)
approach. A regular and globally homogeneous mesh of VO volumes is defined at satellite mean altitude. Satellite measurements are acquired at different altitudes for the same location, so correction for the altitude dependence
is needed. We use an Equivalent Source Dipole (ESD) technique. For each VO and during a given time interval, all measurements are reduced to a unique location,leading to a time series similar to those available at ground
magnetic observatories. To validate this approach, tests are performed using synthetic data at satellite altitude.Several parameters such as the number of ESD, the mesh geometry and the VO lateral and altitude dimensions are
studied. The latter are adjusted in terms of the shortest time period required to have enough measurements inside the VO volume. We then apply our scheme to the first measurements of the ESA SWARM mission. We present
preliminary results over the European area, where we locally compare our VO time series to ground observations, as well as to satellite-based model predictions.