Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

Mori, Hajime; Ishii, Mamoru; Murayama, Yasuhiro; Kubota, Minoru; Sakanoi, Kazuyo; Yamamoto, Masa‐yuki; Monzen, Y.; Lummerzheim, D.; Watkins, B. J.

doi:10.5194/angeo-22-1613-2004

Cited by 38 publications

(20 citation statements)

References 27 publications

(41 reference statements)

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“…The imaging riometer scans an antenna beam in about 200 directions within 1 s to monitor cosmic radio noise variation at 38.2 MHz over a wide field of view of the radio sky. It observes lower ionospheric disturbances over a 400‐km by 400‐km field of view at the 90‐km height with a spatial resolution of about 7° (11 km around the zenith), a sensitivity better than 0.1 dB, and a time resolution of 1 s [ Murayama et al , 1997; Mori et al , 2004].…”

Section: Experimental Techniquementioning

confidence: 99%

Satellite and ground‐based observations of auroral energy deposition and the effects on thermospheric composition during large geomagnetic storms: 1. Great geomagnetic storm of 20 November 2003

et al. 2008

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[1] This report describes thermospheric composition and particle precipitation changes that occurred during the period of the great geomagnetic storm of 20-21 November 2003, an event that was associated with the passage of a magnetic cloud past the Earth. These changes are compared to those observed during geomagnetic activity on 17 November 2003 and during the intervening quieter period. The data used are obtained from (1) ground-based magnetometers, an imaging riometer, a scanning Doppler imaging Fabry-Perot, and photometers from stations in Alaska, (2) photometers from Canadian sites, (3) NOAA POES and DMSP particle sensors, and (4) the TIMED Global Ultraviolet Imager far UV sensor. The composition changes associated with the input of auroral particle and Joule energy showed larger depletions in atomic oxygen on 20 November than on the other nights and greater changes than are seen in the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter (NRLMSIS) model atmosphere. NRLMSIS does better in reproducing the changes during the great magnetic storm with its long duration auroral energy input than during the shorter time duration geomagnetic activity that occurred on 17 November. During the nights with the largest changes in composition the input of Joule energy dominates over auroral particle energy. It is shown that the particle energy distributions associated with the 20-21 November storm in the period around and after the passage of the magnetic cloud had lower average energies and were enhanced at energies below 0.1 keV than those that caused auroral displays on the preceding days.Citation: Hecht, J. H., et al. (2008), Satellite and ground-based observations of auroral energy deposition and the effects on thermospheric composition during large geomagnetic storms: 1.

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Section: Experimental Techniquementioning

confidence: 99%

Satellite and ground‐based observations of auroral energy deposition and the effects on thermospheric composition during large geomagnetic storms: 1. Great geomagnetic storm of 20 November 2003

et al. 2008

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“…[1][2][3][4][5][6][7][8][9][10][11] Plasmas with an excess of superthermal ͑non-Maxwellian͒ electrons are generally characterized by a long tail in the high energy region. [1][2][3][4][5][6][7][8][9][10][11] Plasmas with an excess of superthermal ͑non-Maxwellian͒ electrons are generally characterized by a long tail in the high energy region.…”

Section: Introductionmentioning

confidence: 99%

Oblique electrostatic excitations in a magnetized plasma in the presence of excess superthermal electrons

et al. 2010

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The nonlinear propagation of ion-acoustic waves is considered in a magnetized plasma, composed of kappa distributed electrons and an inertial ion fluid. The fluid-dynamical system of equations governing the dynamics of ion-acoustic waves is reduced to a pseudoenergy-balance equation. The properties of arbitrary amplitude, obliquely propagating ion-acoustic solitary waves are thus investigated via a mechanical-motion analog ͑Sagdeev potential͒ approach. The presence of excess superthermal electrons is shown to influence the nature of magnetized ion-acoustic solitons. The influence on the soliton characteristics of relevant physical parameters such as obliqueness ͑the angle between soliton propagation direction and magnetic field͒, the electron deviation from a Maxwellian ͑"superthermality"͒ and the soliton speed is investigated.

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“…The energy spectra were estimated with a kappa distribution for the case of δ CNA > 0 [cf. Mori et al , 2004] and a Gaussian distribution for the case of δ CNA < 0 [cf. Ono and Hirasawa , 1992] because they were often used to represent the spectra for the diffuse aurora and for the discrete aurora, respectively.…”

Section: Observational Resultsmentioning

confidence: 99%

“…For instance, the ratio of CNA to the auroral (557.7‐nm) intensity has been used as an indicator of electron energy spectrum hardening [ Ansari , 1964; Johansen , 1965; Berkey , 1968]. Recently, Mori et al [2004] compared CNA observed with an imaging riometer with theoretical values estimated from meridian scanning photometer data and demonstrated that kappa or double‐Maxwellian energy distributions explain the observed CNA better than a Maxwellian does. Their results indicate that CNA can be used to estimate the energy spectra, including the high‐energy range.…”

Section: Introductionmentioning

confidence: 99%

Spectral type of auroral precipitating electrons estimated from optical and cosmic noise absorption measurements

et al. 2006

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[1] We estimate the energy spectra of precipitating electrons from optical emissions and cosmic noise absorption (CNA) that were observed with the all-sky imager (ASI) and the 16 Â 16-element imaging riometer at Poker Flat Research Range (PFRR) (65.11°N, 147.42°W), Alaska. Maxwellian energy parameters (peak energy and total energy flux) are derived from the auroral spectroscopic emissions according to the conventional photometric method. We theoretically estimate CNA from the derived Maxwellian spectra and compare it with the observed CNA. The difference between the estimated CNA and the observed (dCNA) is used as an indicator of the difference in high-energy electron flux (E > $20 keV) between the actual energy distribution and Maxwellian. This analysis is carried out for two substorm events in the evening sector to show the temporal and spatial variations of the energy spectra. Event 1 exhibits two arcs of CNA during the growth phase of the substorm. The results for this event suggest that the energy spectra of the two CNA arcs have different shapes, and this difference is revealed in the precipitating electron flux quasi-simultaneously measured by NOAA-17. Event 2 is a typical substorm that consists of growth, expansion, and recovery phases. The dCNA changes throughout the course of the substorm, which can be consistently explained by the energy spectrum variation of the precipitating electrons measured in previous studies. Furthermore, the energy spectra consistent for both optical and CNA data are estimated by assuming a kappa or a Gaussian distribution. This estimation method based on ground observations of the energy spectra has the potential to provide significant information on substorms.

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Energy distribution of precipitating electrons estimated from optical and cosmic noise absorption measurements

Cited by 38 publications

References 27 publications

Satellite and ground‐based observations of auroral energy deposition and the effects on thermospheric composition during large geomagnetic storms: 1. Great geomagnetic storm of 20 November 2003

Satellite and ground‐based observations of auroral energy deposition and the effects on thermospheric composition during large geomagnetic storms: 1. Great geomagnetic storm of 20 November 2003

Oblique electrostatic excitations in a magnetized plasma in the presence of excess superthermal electrons

Spectral type of auroral precipitating electrons estimated from optical and cosmic noise absorption measurements

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