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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 21, issue 2
Nonlin. Processes Geophys., 21, 539–553, 2014
https://doi.org/10.5194/npg-21-539-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 21, 539–553, 2014
https://doi.org/10.5194/npg-21-539-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 24 Apr 2014

Research article | 24 Apr 2014

FLIP-MHD-based model sensitivity analysis

C. Skandrani1, M. E. Innocenti2, L. Bettarini3, F. Crespon1, J. Lamouroux1, and G. Lapenta2 C. Skandrani et al.
  • 1NOVELTIS, Space and Remote Sensing Department, Space Weather Unit, rue du Lac 153, 31670 Labège, France
  • 2Center for Mathematical Plasma Astrophysics, Department of Mathematics, KULeuven – University of Leuven, Celestijnenlaan 200B, bus2400, 3001 Leuven, Belgium
  • 3Fluid and Plasma Dynamics group, Department of Physics, Université Libre de Bruxelles, Campus Plaine – CP231 Boulevard du Triomphe, 1050 Brussels, Belgium

Abstract. The state of the art in the forecast of the background solar wind speed and of the interplanetary magnetic field at Earth is based on the use as boundary conditions for heliospheric models of the input data provided by solar observations. Magnetogram synoptic maps are used to obtain information on the magnetic field configuration at the solar source surface. Magnetic field inputs at the solar source surface thus constitute one of the main external sources of errors in solar wind models. The assimilation of data into forecasting models used in the terrestrial domain showed the ability to control model state errors. A sensitivity study performed through the analysis of the ensemble variances and the representers technique is used here to assess how process and model state errors propagate in a nonlinear two-dimensional MagnetoHydro Dynamic (MHD) system. The aim is to understand the impact of the source surface input parameters on the evolution of MHD heliospheric models and the potentialities of data assimilation techniques in solar wind forecasting. The representer technique in fact allows one to understand how far from the observation point the improvement granted from the assimilation of a measure propagates.

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