Research Article

An integrated data-driven solar wind – CME numerical framework for space weather forecasting

¹ University of Toronto Institute for Aerospace Studies, Toronto, ON M3H 5T6, Canada
² Canadian Hazards Information Service, Natural Resources Canada, Ottawa, ON K1A 0E7 Canada
³ Department of Applied Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada

^* Corresponding author: ljubomir.nikolic@canada.ca

Received: 19 August 2020
Accepted: 18 November 2020

Abstract

The development of numerical models and tools which have operational space weather potential is an increasingly important area of research. This study presents recent Canadian efforts toward the development of a numerical framework for Sun-to-Earth simulations of solar wind disturbances. This modular three-dimensional (3D) simulation framework is based on a semi-empirical data-driven approach to describe the solar corona and an MHD-based description of the heliosphere. In the present configuration, the semi-empirical component uses the potential field source surface (PFSS) and Schatten current sheet (SCS) models to derive the coronal magnetic field based on observed magnetogram data. Using empirical relations, solar wind properties are associated with this coronal magnetic field. Together with a coronal mass ejection (CME) model, this provides inner boundary conditions for a global MHD model which is used to describe interplanetary propagation of the solar wind and CMEs. The proposed MHD numerical approach makes use of advanced numerical techniques. The 3D MHD code employs a finite-volume discretization procedure with limited piecewise linear reconstruction to solve the governing partial-differential equations. The equations are solved on a body-fitted hexahedral multi-block cubed-sphere mesh and an efficient iterative Newton method is used for time-invariant simulations and an explicit time-marching scheme is applied for unsteady cases. Additionally, an efficient anisotropic block-based refinement technique provides significant reductions in the size of the computational mesh by locally refining the grid in selected directions as dictated by the flow physics. The capabilities of the framework for accurately capturing solar wind structures and forecasting solar wind properties at Earth are demonstrated. Furthermore, a comparison with previously reported results and future space weather forecasting challenges are discussed.