SwarmFACE: A Python package for field-aligned currents exploration with Swarm

Abstract

The SwarmFACE package utilizes magnetic field measurements by the Swarm satellites to study systems of field-aligned currents (FACs). Improvements of well-established techniques as well as novel single- and multi-satellite methods or satellite configurations are implemented to extend the characterization of FAC systems beyond the Swarm official Level-2 FAC product. Specifically, the included single-satellite algorithm allows to consider the FAC sheet inclination with respect to the satellite orbit and can work with low- or high-resolution data. For dual-satellite FAC estimation the package provides three algorithms, based on the least-squares, on the singular value decomposition, and on the Cartesian boundary-integral methods. These algorithms offer advantages over the corresponding Level-2 algorithm by providing more stable solutions for ‘extreme’ configurations, e.g. close to the orbital cross-point, and by allowing for a more general geometry of the spacecraft configuration. In addition, the singular value decomposition algorithm adapts itself to the spacecraft configuration, allowing for continuous, dual-satellite based FAC solutions over the entire polar region. Similarly, when Swarm forms a close configuration, the package offers the possibility to estimate the FAC density with a three-satellite method, obtaining additional information, associated to a different (larger) scale. All these algorithms are incorporating a robust framework for FAC error assessment. The SwarmFACE package further provides useful utilities to automatically estimate the auroral oval location or the intervals when Swarm forms a close configuration above the auroral oval. In addition, for each auroral oval crossing, a series of FAC quality indicators, related to the FAC methods’ underlying assumptions, can be estimated, like the current sheet inclination and planarity or the degree of current sheet stationarity.

Gradient calculation techniques for multi-point ionosphere/thermosphere measurements from GDC

“…Shen et al (2012a) and Shen et al (2012b) developed magnetic gradient estimation techniques with particular relevance to measurements in an environment dominated by field-aligned currents. Blagau and Vogt (2019) and Blagau and Vogt (2023) implemented relevant techniques in a publicly available software package based on Python. Of the mentioned works, those described by Shen et al (2012b) and Vogt et al (2020) stand out as general approaches to gradient calculations, both of which utilize some adaptation of the position tensor of the satellite configuration to compute spatial gradients in a least-squares sense.…”