Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition

• URL: http://arxiv.org/abs/2102.01447v1
• Date: Tue, 2 Feb 2021 11:47:07 GMT
• Title: Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition
• Authors: Panagiotis Tigas and T\'eo Bloch and Vishal Upendran and Banafsheh Ferdoushi and Mark C. M. Cheung and Siddha Ganju and Ryan M. McGranaghan and Yarin Gal and Asti Bhatt
• Abstract summary: We develop a Deep Learning model that forecasts in Spherical Harmonics space 2, replacing reliance on MHD models. We evaluate the performance in SuperMAG dataset (improved by 14.53%) and MHD simulations (improved by 24.35%)
• Score: 19.755689078176772
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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