scholarly journals Contribution to the Research of the Effects of Etna Volcano Activity on the Features of the Ionospheric Total Electron Content Behaviour

2021 ◽  
Vol 13 (5) ◽  
pp. 1006
Author(s):  
Ivan Toman ◽  
David Brčić ◽  
Serdjo Kos
Keyword(s):  
Total Electron Content ◽  
Space Weather ◽  
Volcanic Activity ◽  
Reference Station ◽  
Electron Content ◽  
Etna Volcano ◽  
Short Term ◽  
Activity Increase ◽  
Total Electron ◽  
Ionospheric Dynamics

This research represents a contribution to the theory on the coupling of the volcanic activity and the ionospheric dynamics, represented by total electron content (TEC) patterns and their behaviour. The ionospheric response to the activity of the Etna volcano has been analysed using global navigation satellite system (GNSS)-derived TEC values, employing data from International GNSS Service (IGS) reference station near the volcano and on two distant IGS locations. Volcanic activity has been modelled using volcanic radiative power (VRP) data obtained by the Middle InfraRed Observation of Volcanic Activity (MIROVA) system. The estimated minimal night TEC values have been averaged over defined index days of the VRP increase. During the analysed period of 19 years, the volcano activity was categorised according to pre-defined criteria. The influence of current space weather and short-term solar activity on TEC near the volcano was systematically minimised. The results showed mean/median TEC increases of approximately +3 standard deviations from the overall mean values, with peak values placed approximately 5 days before the VRP increase and followed by general TEC depletion around the time of the actual volcanic activity increase. Additionally, TEC oscillation pattern was found over the volcano site with a half-period of 6.25 days. The main interpretation of results indicates that the volcanic activity has modified the ionospheric dynamics within the nearby ionospheric region before the actual VRP increase, and that the residual impact in the volcano’s surrounding area refers to terrestrial endogenous processes and air–earth currents. Those changes can be detected during criteria predefined in the research: during quiet space weather conditions, observing night-time TEC values and within the limits of low short-term solar influence.

scholarly journals Ionospheric Response to the Space Weather Events of 4-10 September 2017: First Chilean Observations

2019 ◽  
Vol 13 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Manuel Bravo ◽  
Carlos Villalobos ◽  
Rodrigo Leiva ◽  
Luis Tamblay ◽  
Pedro Vega-Jorquera ◽  
...  
Keyword(s):  
Total Electron Content ◽  
Space Weather ◽  
Global Navigation Satellite Systems ◽  
Time Interval ◽  
Electron Content ◽  
Dual Frequency ◽  
Field Station ◽  
Total Electron ◽  
Satellite Systems ◽  
Weather Events

Objective: The diurnal variations of several ionospheric characteristics during the Space Weather Events of 4-10 September 2017, for Chilean latitudes, will be reported. Materials and Methods: Observations were made using a recently installed ionosonde at the Universidad de La Serena field station (29°52'S; 71°15’W). Also, reported is the total electron content determined using the upgraded Chilean network of dual-frequency Global Navigation Satellite Systems (GNSS) receivers. Results: Sudden ionospheric disturbances are described in terms of the minimum reflection frequency determined from ionosonde records. An attempt to derive the extent of the effect on high frequency propagation paths in the region is made using simultaneous ionosonde observations at other locations. The geomagnetic storm ionospheric effects are discussed in detail using the observed diurnal variation of maximum electron concentration (NmF2), virtual height of the F-region (h’F/F2) and Total Electron Content (TEC). These are complemented with the time-latitude variation of TEC for the 70°W meridian. Conclusion: It is found that large increases of NmF2, h’F/F2 and TEC observed during 8 September 2017 storm are well described in terms of the evolution of the Equatorial Ionospheric Anomaly (EIA) over the same time interval. Known physical mechanisms are suggested to explain most of the observations.

scholarly journals INVESTIGATION OF THE RELATIONSHIP BETWEEN IONOSPHERIC TEC ANOMALY VARIATIONS AND FAULT TYPES BEFORE THE EARTHQUAKES

2017 ◽  
Vol IV-4/W4 ◽  
pp. 383-388 ◽  
Author(s):  
M. Ulukavak ◽  
M. Yalçınkaya
Keyword(s):  
Total Electron Content ◽  
Space Weather ◽  
Environmental Changes ◽  
Weather Conditions ◽  
Earthquake Precursor ◽  
Strike Slip ◽  
Time Of Arrival ◽  
Electron Content ◽  
Total Electron ◽  
Fault Type

Earthquakes are natural phenomena that shake the earth and cause many damage. Since the time of arrival of the earthquakes cannot be determined directly, some signs before the earthquake should be examined and interpreted by examining the environmental changes. One of the methods used for this is monitoring the ionospheric total electron content (TEC) changes in total electron content unit (TECU). GPS satellites have begun to be used as a means of monitoring ionospheric TEC anomalies before earthquakes since they began to be used as sensors around the world. In this study, three fault type (normal, thrust and strike-slip faulting) of 28 earthquakes with a magnitude greater than 7 (Mw) and the percentage changes of TEC anomalies before the earthquakes were investigated. The ionospheric TEC anomalies before the earthquake were calculated according to the 15-day running median statistical analysis method. Different solar and geomagnetic indices have been investigated to determine the active space weather conditions and quiet days before and after the earthquake. The TEC anomalies were determined during the quiet days before the earthquake by comparing the ionospheric anomalies that occurred before the earthquake after the determination of quiet days with the indices of the space weather conditions. The results show that there is a relationship between fault type and the earthquake precursor percentage changes and were determined as 47.6 % TECU for regions where normal faulting, 50.4 % TECU for regions where thrust faulting, and 44.2 % TECU for regions where strike-slip faulting occurred, respectively.

scholarly journals GPS normalization and preliminary modeling results of total electron content during a midlatitude space weather event

Radio Science ◽  
2001 ◽  
Vol 36 (2) ◽  
pp. 351-361 ◽  
Author(s):  
Jonathan J. Makela ◽  
Michael C. Kelley ◽  
Jan J. Sojka ◽  
Xiaoqing Pi ◽  
Anthony J. Mannucci
Keyword(s):  
Total Electron Content ◽  
Space Weather ◽  
Electron Content ◽  
Weather Event ◽  
Total Electron ◽  
Space Weather Event

scholarly journals The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 316 ◽  
Author(s):  
Rongxin Tang ◽  
Fantao Zeng ◽  
Zhou Chen ◽  
Jing-Song Wang ◽  
Chun-Ming Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Geomagnetic Storm ◽  
Space Weather ◽  
Short Term Memory ◽  
Geomagnetic Storms ◽  
Extreme Condition ◽  
Electron Content ◽  
Navigation Systems ◽  
Short Term ◽  
Total Electron

Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast.

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