Frontiers in Astronomy and Space Sciences (Mar 2023)
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.
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