Spatial characteristics of wave-like structures in diffuse aurora obtained using optical observations

Axelsson, Katarina; Sergienko, T.; Nilsson, Hans; Brändström, Urban; Ebihara, Yusuke; Asamura, Kazushi; Hirahara, Masafumi

doi:10.5194/angeo-30-1693-2012

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…These aurorae have been shown to be associated with electrons ≥ 8 keV scattered into the loss-cone from trapped magnetospheric particles . A statistical analysis of about 500 diffuse stripelike structures recorded during 5 events have found the scale sizes of these stripes to be on average about 13-14 km, and with a separation of about 5-23 km between each other (Axelsson et al, 2012). This separation maps to about 75-345 km in the equatorial plane of the magnetosphere.…”

Section: Morphology Of Non-pulsating Diffuse Aurora Small-scale Strumentioning

confidence: 98%

“…This separation maps to about 75-345 km in the equatorial plane of the magnetosphere. Using ionospheric equivalent current estimates from ground magnetometers, Axelsson et al (2012) concluded that the stripes move equatorward at a speed close to zero in the plasma convection frame (~100-200 m/s). As in the case of the dayside aurora, the fine structures are understood to be a visual manifestation of cold plasma density or magnetic field structures in the equatorial magnetosphere (Axelsson et al, 2012;Ebihara et al, 2010).…”

Section: Morphology Of Non-pulsating Diffuse Aurora Small-scale Strumentioning

confidence: 99%

“…Using ionospheric equivalent current estimates from ground magnetometers, Axelsson et al (2012) concluded that the stripes move equatorward at a speed close to zero in the plasma convection frame (~100-200 m/s). As in the case of the dayside aurora, the fine structures are understood to be a visual manifestation of cold plasma density or magnetic field structures in the equatorial magnetosphere (Axelsson et al, 2012;Ebihara et al, 2010). A further study on nightside SDAs by Sivadas et al (2019) showed the diffuse structures within the energetic electron arc (EEA, Sergeev et al 2012) equatorward of the growth phase arc.…”

Section: Morphology Of Non-pulsating Diffuse Aurora Small-scale Strumentioning

confidence: 99%

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Diffuse and Pulsating Aurora

et al. 2020

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This chapter reviews fundamental properties and recent advances of diffuse and pulsating aurora. Diffuse and pulsating aurora often occurs on closed field lines and involves energetic electron precipitation by wave-particle interaction. After summarizing the definition, large-scale morphology, types of pulsation, and driving processes, we review observation techniques, occurrence, duration, altitude, evolution, small-scale structures, fast modulation, relation to high-energy precipitation, the role of ECH waves, reflected and secondary electrons, ionosphere dynamics, and simulation of wave-particle interaction. Finally we discuss open questions of diffuse and pulsating aurora.

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Section: Morphology Of Non-pulsating Diffuse Aurora Small-scale Strumentioning

confidence: 98%

Section: Morphology Of Non-pulsating Diffuse Aurora Small-scale Strumentioning

confidence: 99%

Section: Morphology Of Non-pulsating Diffuse Aurora Small-scale Strumentioning

confidence: 99%

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Diffuse and Pulsating Aurora

et al. 2020

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“…In this paper, we call these key local structures (KLS). Extracting the concerned KLS can not only help classify the auroral images but also provide the position information to statistically analyze both spatial and morphological evolution of aurora (Axelsson et al, ; Niu et al, ).…”

Section: Introductionmentioning

confidence: 99%

Extracting Auroral Key Local Structures From All‐Sky Auroral Images by Artificial Intelligence Technique

et al. 2019

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Extracting auroral key local structures (KLS) containing both morphological information and spatial location from large amount of auroral images is the key for automatic auroral classification and event recognition and thus is very important for improving the efficiency of aurora study. However, it is labor‐intensive for human experts and challenging for computer execution because of the blurry boundary and inhomogeneous luminosity of aurora. In this work, we formulate the automatic KLS extraction to learn a mapping from an all‐sky auroral image to a KLS mask style with artificial intelligence technique. We explore the Cycle‐consistent Generative Adversarial Network to generate the KLS masks based on the unpaired training data set consisting of 2508 all‐sky auroral images observed between years 2003–2009 at the Arctic Yellow River Station, of which only 200 images were manually annotated as the ground truth of KLS masks. The extracted KLS are consistent with human visual perception, and the intersection‐over‐union metric is significantly improved by at least 17% than previous method by Niu et al. (2018) (https://doi.org/10.1109/TGRS.2018.2848725). Classification accuracy has been improved when combining the KLS masks further indicates the effectiveness and value of this work.

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Estimating energy spectra of electron precipitation above auroral arcs from ground‐based observations with radar and optics

et al. 2013

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[1] In 2008, coordinated radar-optical auroral observations were organized in Northern Scandinavia using the European Incoherent Scatter Radar (EISCAT) and the Auroral Large Imaging System (ALIS). A bright auroral arc was imaged on 5 March 2008 from four ground-based stations, remaining stable between 18:41 and 18:44 UT and coinciding with increased electron densities. This work presents a unified inversion framework deriving the electron energy spectrum from either optical (N + 2 1NG(0, 1) emission at 4278 Å) or radar (electron density) observations. An updated forward model of the ionosphere based on a 1-D kinetic Monte Carlo model is described, characterizing the linear system to invert. The 3-D blue volume emission rate is first estimated with an iterative reconstruction technique. Also presented is a novel way to calculate an accurate initial guess for auroral tomography taking into account the horizontal/vertical nonuniformity of the emission region. This technique supersedes the often-used Chapman profile as initial guess when high spatial resolution is needed. The second step, performed for the first time with ALIS optical observations, uses the forward model to retrieve from the emission rates the 2-D latitude/longitude map of the electron energy spectrum. The same model and inversion methods are finally applied to the EISCAT electron density profiles to derive the temporal energy spectrum of precipitating electrons along the magnetic zenith. Energy spectra from radar and optics are in good agreement. Results suggest that the arc is generated by a complex energy spectrum reminiscent of dispersive Alfvén waves with two main peaks (2.5, 6 keV) and a typical latitudinal width of 7.5 km.Citation: Simon Wedlund, C., H. Lamy, B. Gustavsson, T. Sergienko, and U. Brändström (2013), Estimating energy spectra of electron precipitation above auroral arcs from ground-based observations with radar and optics,

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Spatial characteristics of wave-like structures in diffuse aurora obtained using optical observations

Cited by 4 publications

References 21 publications

Diffuse and Pulsating Aurora

Diffuse and Pulsating Aurora

Extracting Auroral Key Local Structures From All‐Sky Auroral Images by Artificial Intelligence Technique

Estimating energy spectra of electron precipitation above auroral arcs from ground‐based observations with radar and optics

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