TY - JOUR
T1 - Inverting magnetic meridian data using nonlinear optimization
AU - Connors, Martin
AU - Rostoker, Gordon
N1 - Funding Information:
This work was supported by NSERC and in part by the Canada Research Chairs program. The CANOPUS chain was operated by the Canadian Space Agency. We thank Andrei Kotikov for supplying Russian data used for the April 1, 1986 study to the CDAW-9 project. Polaris data were obtained from NRCan. We thank the institutes who maintain the IMAGE magnetometer array and Eric Donovan and Emma Spanswick of the University of Calgary for THEMIS keograms and calibrations, from cameras supported by the Canadian Space Agency. We also thank the AMPERE team and the AMPERE Science Center for providing the iridium-derived data products and Haje Korth for special effort in that regard. Cape Dorset magnetic data was supplied by Erik Steinmetz and Mark Engetbretson of Augsburg College, via CDAWeb.
Publisher Copyright:
© 2015 Connors and Rostoker.
PY - 2015/12/26
Y1 - 2015/12/26
N2 - 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.
AB - 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.
KW - Current systems
KW - Equivalent current
KW - Geomagnetism
KW - Geophysical inversion techniques
KW - Nonlinear optimization
KW - Substorms
UR - http://www.scopus.com/inward/record.url?scp=84942587962&partnerID=8YFLogxK
U2 - 10.1186/s40623-015-0315-y
DO - 10.1186/s40623-015-0315-y
M3 - Journal Article
AN - SCOPUS:84942587962
SN - 1343-8832
VL - 67
JO - Earth, Planets and Space
JF - Earth, Planets and Space
IS - 1
M1 - 155
ER -