Robust Adaptive Spacecraft Array Derivative Analysis

Vogt, Joachim; Blăgău, A.; Pick, Leonie

doi:10.1029/2019ea000953

Cited by 5 publications

(15 citation statements)

References 21 publications

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“…Note that in Vogt et al (2013) the symbol R is used for the position tensor. In this paper we adhere to the notations from Vogt et al (2020), where R designates the position matrix and the position tensor is indicated by R pos (see below).…”

Section: Dual-satellite Fac Estimation By Least Squaresmentioning

confidence: 99%

“…The dual-satellite SVD algorithm from SwarmFACE represents an adaptation of the more general RASADA (from Robust Adaptive Spacecraft Array Derivative Analysis) method and code developed in Vogt et al (2020). RASADA allows to estimate the spatial derivative of physical quantities and their corresponding errors from an array of arbitrary numbers of satellites/observation points.…”

Section: Dual-satellite Fac Estimation By Singular Value Decompositionmentioning

confidence: 99%

“…The SwarmFACE package provides Python programs to generate novel or refined FAC estimates based on Swarm observations, together with appropriate FAC quality indicators. The algorithms are based on novel methods (Vogt et al, 2009(Vogt et al, , 2013(Vogt et al, , 2020 or are adapted versions of well-established ones (for example the dual-satellite Boundary Integral method from Ritter et al (2013), the single-satellite Finite Differencing from e.g. Luhr et al (1996), or the Minimum Variance Analysis from Sonnerup and Cahill (1967)).…”

Section: Introductionmentioning

confidence: 99%

“…when the satellite cross-track separation is much smaller than the along-track distance, resulting in a four-point configuration close to a line; see Section 2.2), being thus able to provide continuous, dual-satellite based, FAC solutions, i.e. no data gaps near to the orbital cross-points (Vogt et al, 2020). • All dual-satellite algorithms can work on irregular configuration geometries (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…Those programs were refined and new ones were developed during the implementation of the ESA SIFACIT project, that aimed to tap the full potential offered by the Swarm measurements for FAC exploration. Originally written in IDL as part of a larger package designed to analyze both Cluster and Swarm multi-satellite/multiinstrument measurements, the routines have been extensively tested and their predictions compared with the values of the FAC Level-2 products (Blagau and Vogt, 2019;Vogt et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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

Blăgău

Vogt

2023

Front. Astron. Space Sci.

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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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Section: Dual-satellite Fac Estimation By Least Squaresmentioning

confidence: 99%

Section: Dual-satellite Fac Estimation By Singular Value Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Blăgău

Vogt

2023

Front. Astron. Space Sci.

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Multi‐Spacecraft Magnetic Field Reconstructions: A Cross‐Scale Comparison of Methods

Broeren,

Klein,

TenBarge

2024

Earth and Space Science

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Space plasma studies frequently use in situ magnetic field measurements taken from many spacecraft simultaneously. A useful data product of these measurements is the reconstructed magnetic field in a volume near the spacecraft observatory. We compare a standard Linear method of computing the magnetic field at arbitrary spatial points to two novel approaches: a Radial Basis Function interpolation and a time‐dependent 2D inverse distance weighted interpolation scheme called Timesync. These three methods, which only require in situ measurements of the magnetic fields and bulk plasma velocities at a sparse set of spatial points, are implemented on synthetic data drawn from a time‐evolving numerical simulation of plasma turbulence. We compare both the topology of the reconstructed field to the ground truth of the simulation and the statistics of the fluctuations found in the reconstructed field to those from the simulated turbulence. We conclude that the Radial Basis Function and Timesync methods outperform the Linear method in both topological and statistical comparisons.

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Estimation of the Error in the Calculation of the Pressure‐Strain Term: Application in the Terrestrial Magnetosphere

et al. 2023

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Calculating the pressure‐strain terms has recently been performed to quantify energy conversion between the bulk flow energy and the internal energy of plasmas. It has been applied to numerical simulations and satellite data from the Magnetospheric MultiScale Mission. The method requires spatial gradients of the velocity and the use of the full pressure tensor. Here we present a derivation of the errors associated with calculating the pressure‐strain terms from multi‐spacecraft measurements and apply it to previously studied examples of magnetic reconnection at the magnetopause and the magnetotail. The errors are small in a dense magnetosheath event but much larger in the more tenuous magnetotail. This is likely due to larger counting statistics in the dense plasma at the magnetopause than in the magnetotail. The propagated errors analyzed in this work are important to understand uncertainties of energy conversion measurements in space plasmas and have applications to current and future multi‐spacecraft missions.This article is protected by copyright. All rights reserved.

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Robust Adaptive Spacecraft Array Derivative Analysis

Cited by 5 publications

References 21 publications

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

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

Multi‐Spacecraft Magnetic Field Reconstructions: A Cross‐Scale Comparison of Methods

Estimation of the Error in the Calculation of the Pressure‐Strain Term: Application in the Terrestrial Magnetosphere

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