Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition

TL;DR

This paper introduces a deep learning approach that forecasts the Earth's magnetic field perturbations in spherical harmonics space, offering improved accuracy and global coverage over traditional MHD models and sparse station data.

Contribution

The paper presents a novel deep learning model that predicts magnetic field perturbations in spherical harmonics space, reducing reliance on computationally intensive MHD simulations and enhancing global forecasting capabilities.

Findings

Forecast accuracy improved by 14.53% on SuperMAG data.

Forecast accuracy improved by 24.35% on MHD simulations.

Spherical harmonics can reliably reconstruct global magnetic fields from sparse data.

Abstract

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.