Inverting magnetic meridian data using nonlinear optimization

Abstract

A nonlinear optimization algorithm coupled with a model of auroral current systems allows derivation of physical parameters from data and is the basis of a new inversion technique. We refer to this technique as automated forward modeling (AFM), with the variant used here being automated meridian modeling (AMM). AFM is applicable on scales from regional to global, yielding simple and easily understood output, and using only magnetic data with no assumptions about electrodynamic parameters. We have found the most useful output parameters to be the total current and the boundaries of the auroral electrojet on a meridian densely populated with magnetometers, as derived by AMM. Here, we describe application of AFM nonlinear optimization to magnetic data and then describe the use of AMM to study substorms with magnetic data from ground meridian chains as input. AMM inversion results are compared to optical data, results from other inversion methods, and field-aligned current data from AMPERE. AMM yields physical parameters meaningful in describing local electrodynamics and is suitable for ongoing monitoring of activity. The relation of AMM model parameters to equivalent currents is discussed, and the two are found to compare well if the field-aligned currents are far from the inversion meridian.

The AUTUMNX magnetometer meridian chain in Québec, Canada

“…To assure temperature stability, the sensor head is preferably buried in soil, which posed some constraints in the rocky landscapes of northern Québec. Some of the sites of the temporary network Polaris (Connors and Rostoker 2015) were in the same towns as AUTUMNX, but on rock, which meant that the same sites could not be reused.…”

“…The Akulivik site seems noisy, especially in the B X component, which is thought to be due to a geologically anomalous site. This issue is being investigated with data from the Polaris network (Connors and Rostoker 2015).…”

“…Before proceeding to discuss the events, we describe a tool known as Automated Meridian Modeling (Connors and Rostoker 2015) which is useful in interpreting magnetic meridian chain data. Readers are referred to the Fig.…”

“…The enhanced coverage in eastern North America now offers good opportunities for Antarctic conjugacy, and several stations near enough to the GOES East footpoint for satellite conjugate studies to be productive. For example, magnetic inversion on a meridian chain can establish electrojet positions and strengths (Connors and Rostoker 2015) and allow their relation to geosynchronous field variations to be investigated (see case study below). In order to determine phase shifts associated with fieldaligned resonances which may be associated with pulsations detected at geosynchronous orbit, a meridian chain near the footpoint is needed (Ziesolleck et al 1996).…”

Special issue “The 12th International Conference on Substorms”

“…The topics and related papers are: control of magnetospheric conditions by solar wind parameters (Kubyshkina et al 2015;Sergeev et al 2015;Troshichev and Sormakov 2015), modeling of reconnection and instabilities in the tail (Birn et al 2015;Pritchett 2015;Uchino and Machida 2015), relationships between relativistic electrons and pulsations in the inner magnetosphere Hajra et al 2015;Teramoto et al 2016), the effects of substorms on ionospheric irregularities and currents (Berngardt et al 2015;Cherniak and Zakharenkova 2015;Connors and Rostoker 2015), auroral disturbances during substorms Tanaka et al 2015), and ground magnetometer chains (Connors et al 2016). In addition, a review paper by Akasofu (2015) based on his presentation at ICS-12 was published in Progress in Earth and Planetary Science.…”