Quiet, Discrete Auroral Arcs—Observations

Karlsson, Tomas; Andersson, L.; Gillies, D. M.; Lynch, K. A.; Marghitu, Octav; Partamies, Noora; Sivadas, Nithin; Wu, Jiashu

doi:10.1007/s11214-020-0641-7

Cited by 39 publications

(37 citation statements)

References 240 publications

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“…Alternatively, the E-field modulation could originate from the magnetosphere. A candidate mechanism is that the shear between the strong flow in the high E field adjacent to the arc and the slower flow in the arc itself leads to a Kelvin-Helmholz instability, whose phase speed would be between the slow and fast flows (see, e.g., Keskinen et al, 1988). For E ⊥ ≈ 70 mV m −1 corresponding to 1400 m s −1 , the phase speed of Kelvin-Helmholtz waves would be several hundred metres per second, which is roughly the observed value.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of fragmented aurora-like emissions (FAEs) observed on Svalbard

et al. 2021

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Abstract. This study analyses the observations of a new type of small-scale aurora-like feature, which is further referred to as fragmented aurora-like emission(s) (FAEs). An all-sky camera captured these FAEs on three separate occasions in 2015 and 2017 at the Kjell Henriksen Observatory near the arctic town of Longyearbyen, Svalbard, Norway. A total of 305 FAE candidates were identified. They seem to appear in two categories – randomly occurring individual FAEs and wave-like structures with regular spacing between FAEs alongside auroral arcs. FAEs show horizontal sizes typically below 20 km, a lack of field-aligned emission extent, and short lifetimes of less than a minute. Emissions were observed at the 557.7 nm line of atomic oxygen and at 673.0 nm (N2; first positive band system) but not at the 427.8 nm emission of N2+ or the 777.4 nm line of atomic oxygen. This suggests an upper limit to the energy that can be produced by the generating mechanism. Their lack of field-aligned extent indicates a different generation mechanism than for aurorae, which are caused by particle precipitation. Instead, these FAEs could be the result of excitation by thermal ionospheric electrons. FAE observations are seemingly accompanied by elevated electron temperatures between 110–120 km and increased ion temperatures at F-region altitudes. One possible explanation for this is Farley–Buneman instabilities of strong local currents. In the present study, we provide an overview of the observations and discuss their characteristics and potential generation mechanisms.

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Section: Discussionmentioning

confidence: 99%

Characteristics of fragmented aurora-like emissions (FAEs) observed on Svalbard

et al. 2021

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“…Row 3 in Figure 3 illustrates samples of arcs . Auroral arcs run parallel to the auroral oval and the magnetic latitudes (Karlsson et al, 2020). They result from quasi‐static particle acceleration in a region close to the ionosphere (Lysak et al, 2020), and they magnetically map to the plasma sheet.…”

Section: Methodsmentioning

confidence: 99%

Auroral Image Classification With Deep Neural Networks

et al. 2020

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Results from a study of automatic aurora classification using machine learning techniques are presented. The aurora is the manifestation of physical phenomena in the ionosphere-magnetosphere environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral images in an objective, organized, and repeatable manner. Although previous studies have presented tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a high precision (>90%). This work considers seven auroral subclasses: breakup, colored, arcs, discrete, patchy, edge, and faint. Six different deep neural network architectures have been tested along with the well-known classification algorithms: k-nearest neighbor (KNN) and a support vector machine (SVM). A set of clean nighttime color auroral images, without clearly ambiguous auroral forms, moonlight, twilight, clouds, and so forth, were used for training and testing the classifiers. The deep neural networks generally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highest performance with an average classification precision of 92%.

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“…The midnight sector current distribution in Fig. 6 depicts the substorm current wedge (McPherron et al, 1973;Birn et al, 1999;Keiling et al, 2009) consisting of thick downward/upward current filaments on the dawn/dusk side, connected in the ionosphere by the substorm (westward) electrojet. An additional FAC distribution (not shown in Fig.…”

Section: Field-aligned Currentsmentioning

confidence: 99%

Lower-thermosphere–ionosphere (LTI) quantities: current status of measuring techniques and models

et al. 2021

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Abstract. The lower-thermosphere–ionosphere (LTI) system consists of the upper atmosphere and the lower part of the ionosphere and as such comprises a complex system coupled to both the atmosphere below and space above. The atmospheric part of the LTI is dominated by laws of continuum fluid dynamics and chemistry, while the ionosphere is a plasma system controlled by electromagnetic forces driven by the magnetosphere, the solar wind, as well as the wind dynamo. The LTI is hence a domain controlled by many different physical processes. However, systematic in situ measurements within this region are severely lacking, although the LTI is located only 80 to 200 km above the surface of our planet. This paper reviews the current state of the art in measuring the LTI, either in situ or by several different remote-sensing methods. We begin by outlining the open questions within the LTI requiring high-quality in situ measurements, before reviewing directly observable parameters and their most important derivatives. The motivation for this review has arisen from the recent retention of the Daedalus mission as one among three competing mission candidates within the European Space Agency (ESA) Earth Explorer 10 Programme. However, this paper intends to cover the LTI parameters such that it can be used as a background scientific reference for any mission targeting in situ observations of the LTI.

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Quiet, Discrete Auroral Arcs—Observations

Cited by 39 publications

References 240 publications

Characteristics of fragmented aurora-like emissions (FAEs) observed on Svalbard

Characteristics of fragmented aurora-like emissions (FAEs) observed on Svalbard

Auroral Image Classification With Deep Neural Networks

Lower-thermosphere–ionosphere (LTI) quantities: current status of measuring techniques and models

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