A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data
Aurora, the spectacular phenomenon commonly occurring in high latitudes, is caused by the precipitation of energetic particles penetrating the Earth’s atmosphere. Being the result of solar-terrestrial interactions, electron precipitation significantly contributes to auroral production. To evaluate i...
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ftmdpi:oai:mdpi.com:/2072-4292/12/14/2259/ 2023-08-20T04:02:29+02:00 A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data Wanqiu Kong Zejun Hu Jiaji Wu Tan Qu Gwanggil Jeon agris 2020-07-14 application/pdf https://doi.org/10.3390/rs12142259 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs12142259 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 12; Issue 14; Pages: 2259 aurora atmospheric electron transport auroral energy hyperspectral data DMSP SSJ5 Text 2020 ftmdpi https://doi.org/10.3390/rs12142259 2023-07-31T23:46:22Z Aurora, the spectacular phenomenon commonly occurring in high latitudes, is caused by the precipitation of energetic particles penetrating the Earth’s atmosphere. Being the result of solar-terrestrial interactions, electron precipitation significantly contributes to auroral production. To evaluate its magnitude, a physical quantity describing the characteristics of precipitating auroral electrons—their characteristic energy—is adopted. In this paper, this quantity is derived from joint data observed by the ground-based auroral spectroscopic imager located in Antarctica Zhongshan Station and the particle detectors “Special Sensor J5 (SSJ5)” on the Defense Meteorological Satellite Program (DMSP) satellites. A postprocessing scheme of ground-based spectral data is proposed to infer the characteristic energy that successively uses classical brute-force, recursive brute-force and self-consistent approximation strategies for step-up speed improvement. Then, the inferred characteristic energies are compared to the average energies calibrated from the relevant electron data detected by SSJ5 to confirm whether this inference is valid. Regarding DMSP F18/SSJ5, these two energy estimations about auroral electrons deviate slightly from each other and show a strong linear relationship. It sheds light on further applications of the valuable aurora spectral data. Text Antarc* Antarctica MDPI Open Access Publishing Zhongshan ENVELOPE(76.371,76.371,-69.373,-69.373) Zhongshan Station ENVELOPE(76.371,76.371,-69.373,-69.373) Remote Sensing 12 14 2259 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
aurora atmospheric electron transport auroral energy hyperspectral data DMSP SSJ5 |
spellingShingle |
aurora atmospheric electron transport auroral energy hyperspectral data DMSP SSJ5 Wanqiu Kong Zejun Hu Jiaji Wu Tan Qu Gwanggil Jeon A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data |
topic_facet |
aurora atmospheric electron transport auroral energy hyperspectral data DMSP SSJ5 |
description |
Aurora, the spectacular phenomenon commonly occurring in high latitudes, is caused by the precipitation of energetic particles penetrating the Earth’s atmosphere. Being the result of solar-terrestrial interactions, electron precipitation significantly contributes to auroral production. To evaluate its magnitude, a physical quantity describing the characteristics of precipitating auroral electrons—their characteristic energy—is adopted. In this paper, this quantity is derived from joint data observed by the ground-based auroral spectroscopic imager located in Antarctica Zhongshan Station and the particle detectors “Special Sensor J5 (SSJ5)” on the Defense Meteorological Satellite Program (DMSP) satellites. A postprocessing scheme of ground-based spectral data is proposed to infer the characteristic energy that successively uses classical brute-force, recursive brute-force and self-consistent approximation strategies for step-up speed improvement. Then, the inferred characteristic energies are compared to the average energies calibrated from the relevant electron data detected by SSJ5 to confirm whether this inference is valid. Regarding DMSP F18/SSJ5, these two energy estimations about auroral electrons deviate slightly from each other and show a strong linear relationship. It sheds light on further applications of the valuable aurora spectral data. |
format |
Text |
author |
Wanqiu Kong Zejun Hu Jiaji Wu Tan Qu Gwanggil Jeon |
author_facet |
Wanqiu Kong Zejun Hu Jiaji Wu Tan Qu Gwanggil Jeon |
author_sort |
Wanqiu Kong |
title |
A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data |
title_short |
A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data |
title_full |
A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data |
title_fullStr |
A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data |
title_full_unstemmed |
A Comparative Study of Estimating Auroral Electron Energy from Ground-Based Hyperspectral Imagery and DMSP-SSJ5 Particle Data |
title_sort |
comparative study of estimating auroral electron energy from ground-based hyperspectral imagery and dmsp-ssj5 particle data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12142259 |
op_coverage |
agris |
long_lat |
ENVELOPE(76.371,76.371,-69.373,-69.373) ENVELOPE(76.371,76.371,-69.373,-69.373) |
geographic |
Zhongshan Zhongshan Station |
geographic_facet |
Zhongshan Zhongshan Station |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_source |
Remote Sensing; Volume 12; Issue 14; Pages: 2259 |
op_relation |
Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs12142259 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs12142259 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
14 |
container_start_page |
2259 |
_version_ |
1774712941264764928 |