Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm

During emergency events, we could significantly depend on the stable operation of radio communication, navigation, and radars. The ionosphere, especially its auroral regions, significantly influences radio systems, which is why scientists and engineers create systems to monitor these regions. Using...

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Main Authors: Ilya K. Edemskiy, Yury V. Yasyukevich
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/14/21/5486
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author Ilya K. Edemskiy
Yury V. Yasyukevich
author_facet Ilya K. Edemskiy
Yury V. Yasyukevich
author_sort Ilya K. Edemskiy
collection DOAJ
description During emergency events, we could significantly depend on the stable operation of radio communication, navigation, and radars. The ionosphere, especially its auroral regions, significantly influences radio systems, which is why scientists and engineers create systems to monitor these regions. Using data from the global GNSS network, we analyzed the 10 strongest magnetic storms of solar cycle 24: five coronal mass ejection-driven (CME-driven) and five high-speed stream-driven (HSS-driven) storms. The analysis was based on the calculation of the standard deviation of the total electron content (TEC) derivative (rate of TEC index, ROTI). Under all the storms, the ROTI featured similar dynamics: the average ROTI reaches the highest values during the main phase, and the higher the intensity is, the more intense and equatorward the average ROTI registered. The highest cross-correlations are observed with a lag of 1 h, between the IMF z-component Bz and the magnetic latitude where the highest ROTI values appear. The auroral electrojet (SME index) shows the highest impact on the ROTI dynamics. An increase in the space weather indices (in absolute value) is accompanied by a decrease in the latitude where the maximal ROTI occurs. We found that the peculiarities of a storm affect the ROTI dynamics: all the CME-driven storms feature a high cross-correlation (>0.75) between the IMF z-component Bz and the magnetic latitude where the highest ROTI appears, while the HSS-driven storms feature a lower cross-correlation (<0.75) between them. The difference in duration of similar (by maximal values of geomagnetic indices) HSS- and CME-driven storms could produce differences in the highest ROTI values. Correlations show that compared to HSS-driven storms, CME-driven ones more directly impact the ROTI values and locations of regions with a high ROTI.
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spelling doaj.art-6fa2007eb9a745a3bc385a75db4f4bb72023-11-24T06:39:48ZengMDPI AGRemote Sensing2072-42922022-10-011421548610.3390/rs14215486Auroral Oval Boundary Dynamics on the Nature of Geomagnetic StormIlya K. Edemskiy0Yury V. Yasyukevich1Department of Near-Earth Space Physics, Institute of Solar-Terrestrial Physics SB RAS, Irkutsk 664033, RussiaDepartment of Near-Earth Space Physics, Institute of Solar-Terrestrial Physics SB RAS, Irkutsk 664033, RussiaDuring emergency events, we could significantly depend on the stable operation of radio communication, navigation, and radars. The ionosphere, especially its auroral regions, significantly influences radio systems, which is why scientists and engineers create systems to monitor these regions. Using data from the global GNSS network, we analyzed the 10 strongest magnetic storms of solar cycle 24: five coronal mass ejection-driven (CME-driven) and five high-speed stream-driven (HSS-driven) storms. The analysis was based on the calculation of the standard deviation of the total electron content (TEC) derivative (rate of TEC index, ROTI). Under all the storms, the ROTI featured similar dynamics: the average ROTI reaches the highest values during the main phase, and the higher the intensity is, the more intense and equatorward the average ROTI registered. The highest cross-correlations are observed with a lag of 1 h, between the IMF z-component Bz and the magnetic latitude where the highest ROTI values appear. The auroral electrojet (SME index) shows the highest impact on the ROTI dynamics. An increase in the space weather indices (in absolute value) is accompanied by a decrease in the latitude where the maximal ROTI occurs. We found that the peculiarities of a storm affect the ROTI dynamics: all the CME-driven storms feature a high cross-correlation (>0.75) between the IMF z-component Bz and the magnetic latitude where the highest ROTI appears, while the HSS-driven storms feature a lower cross-correlation (<0.75) between them. The difference in duration of similar (by maximal values of geomagnetic indices) HSS- and CME-driven storms could produce differences in the highest ROTI values. Correlations show that compared to HSS-driven storms, CME-driven ones more directly impact the ROTI values and locations of regions with a high ROTI.https://www.mdpi.com/2072-4292/14/21/5486ionosphereauroral ovalmagnetic stormCMEHSSGNSS
spellingShingle Ilya K. Edemskiy
Yury V. Yasyukevich
Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm
Remote Sensing
ionosphere
auroral oval
magnetic storm
CME
HSS
GNSS
title Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm
title_full Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm
title_fullStr Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm
title_full_unstemmed Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm
title_short Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm
title_sort auroral oval boundary dynamics on the nature of geomagnetic storm
topic ionosphere
auroral oval
magnetic storm
CME
HSS
GNSS
url https://www.mdpi.com/2072-4292/14/21/5486
work_keys_str_mv AT ilyakedemskiy auroralovalboundarydynamicsonthenatureofgeomagneticstorm
AT yuryvyasyukevich auroralovalboundarydynamicsonthenatureofgeomagneticstorm