Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition

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 (S...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Tigas, Panagiotis, Bloch, Téo, Upendran, Vishal, Ferdoushi, Banafsheh, Cheung, Mark C M, Ganju, Siddha, McGranaghan, Ryan M, Yarin Gal, Bhatt, Asti
Format: Article
Language:English
Subjects:
Computational fluid dynamics
Forecasting
Geomagnetic field
Ground stations
Magnetic fields
Magnetism
Magnetohydrodynamics
Mathematical models
Modelling
Performance evaluation
Simulation
Solar magnetic field
Solar wind
Spherical harmonics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422