Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
This work provides a new method for global earth magnetic field modeling and forecasting, which is important for space weather prediction and related applications.
This paper addresses the challenge of modeling and forecasting solar wind-driven global magnetic field perturbations. The authors developed a Deep Learning model that forecasts in Spherical Harmonics space, achieving a 14.53% improvement on the SuperMAG dataset and a 24.35% improvement on MHD simulations.
Modeling and forecasting the solar wind-driven global magnetic field perturbations is an open challenge. Current approaches depend on simulations of computationally demanding models like the Magnetohydrodynamics (MHD) model or sampling spatially and temporally through sparse ground-based stations (SuperMAG). In this paper, we develop a Deep Learning model that forecasts in Spherical Harmonics space 2, replacing reliance on MHD models and providing global coverage at one minute cadence, improving over the current state-of-the-art which relies on feature engineering. We evaluate the performance in SuperMAG dataset (improved by 14.53%) and MHD simulations (improved by 24.35%). Additionally, we evaluate the extrapolation performance of the spherical harmonics reconstruction based on sparse ground-based stations (SuperMAG), showing that spherical harmonics can reliably reconstruct the global magnetic field as evaluated on MHD simulation.