Abstract
Observations of the geomagnetic field taken at Earth’s surface and at satellite altitude are combined to construct continuous models of the geomagnetic field and its secular variation from 1957 to 2020. From these parent models, we derive candidate main field models for the epochs 2015 and 2020 to the 13th generation of the International Geomagnetic Reference Field (IGRF). The secular variation candidate model for the period 2020–2025 is derived from a forecast of the secular variation in 2022.5, which results from a multi-variate singular spectrum analysis of the secular variation from 1957 to 2020.
Highlights
The International Association of Geomagnetism and Aeronomy (IAGA) regularly releases the International Geomagnetic Reference Field (IGRF), which is a mathematical description of Earth’s main magnetic field and its secular variation
The second technique is based on a method for constructing core field models that satisfy the frozen-flux radial magnetic induction equation on the core-mantle boundary (CMB) by imposing the field evolution to be entirely due to advection of the magnetic field at the core surface (Lesur et al 2010; Wardinski and Lesur 2012), which we refer to as the kinematic field model
Geomagnetic field modeling we summarize the derivation of a parent geomagnetic main field model from which we deduce an IGRF candidate model
Summary
The International Association of Geomagnetism and Aeronomy (IAGA) regularly releases the International Geomagnetic Reference Field (IGRF), which is a mathematical description of Earth’s main magnetic field and its secular variation. We combine geomagnetic field observations taken at Earth’s surface and at satellite altitude to construct continuous models of the geomagnetic core field and its secular variation between 1957 and 2020. From these models, candidate models for the IGRF (Alken et al 2020), i.e., main field models for the epochs 2015 and 2020 and a secular variation model for the period 2020 to 2025 centered at 2022.5 are derived. The latter method could be understood as a simple data assimilation approach, where the diffusion-less induction equation and assumptions about the dynamical regime of the core flow form the priors, and observations define their likelihood
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