Data‐driven modeling of the solar wind from 1 Rs to 1 AU

Abstract

AbstractWe present here a time‐dependent three‐dimensional magnetohydrodynamic (MHD) solar wind simulation from the solar surface to the Earth's orbit driven by time‐varying line‐of‐sight solar magnetic field data. The simulation is based on the three‐dimensional (3‐D) solar‐interplanetary (SIP) adaptive mesh refinement (AMR) space‐time conservation element and solution element (CESE) MHD (SIP‐AMR‐CESE MHD) model. In this simulation, we first achieve the initial solar wind background with the time‐relaxation method by inputting a potential field obtained from the synoptic photospheric magnetic field and then generate the time‐evolving solar wind by advancing the initial 3‐D solar wind background with continuously varying photospheric magnetic field. The model updates the inner boundary conditions by using the projected normal characteristic method, inputting the high‐cadence photospheric magnetic field data corrected by solar differential rotation, and limiting the mass flux escaping from the solar photosphere. We investigate the solar wind evolution from 1 July to 11 August 2008 with the model driven by the consecutive synoptic maps from the Global Oscillation Network Group. We compare the numerical results with the previous studies on the solar wind, the solar coronal observations from the Extreme ultraviolet Imaging Telescope board on Solar and Heliospheric Observatory, and the measurements from OMNI at 1 astronomical unit (AU). Comparisons show that the present data‐driven MHD model's results have overall good agreement with the large‐scale dynamical coronal and interplanetary structures, including the sizes and distributions of the coronal holes, the positions and shapes of the streamer belts, the heliocentric distances of the Alfvénic surface, and the transitions of the solar wind speeds. However, the model fails to capture the small‐sized equatorial holes, and the modeled solar wind near 1 AU has a somewhat higher density and weaker magnetic field strength than observed. Perhaps better preprocessing of high‐cadence observed photospheric magnetic field (particularly 3‐D global measurements), combined with plasma measurements and higher resolution grids, will enable the data‐driven model to more accurately capture the time‐dependent changes of the ambient solar wind for further improvements. In addition, other measures may also be needed when the model is employed in the period of high solar activity.