Identification of ionospheric earthquake precursors in Kamchatka region based on correlation analysis

Сейсмическая активность является одним из источников изменчивости ионосферы. В данной работе на основе методики [1] исследовано изменение концентрации электронов в ионосфере, предшествующее наступлению сильных землетрясений с M ≥ 6.0 в Камчатском регионе. Данная методика основана на вычислении коэффициента корреляции между значениями критической частоты foF2 двух ионосферных станций, одна из которых расположена внутри зоны подготовки землетрясения, а другая – за ее пределами. Рассмотрены данные, полученные на двух станциях PETROPAVLOVSK (PK553) и EARECKSON (EA653) за период 01.09.2018–30.04.2021 гг. Статистический анализ критических частот foF2 показал, что для 6 из 8 землетрясений с M ≥ 6.0, произошедших на глубинах до 100 км и эпицентральных расстояниях до 500 км от расположения PK553, за 1–12 суток до их наступления предшествовало заметное уменьшение коэффициента корреляции. Seismic activity is one of the sources of ionospheric variability. In this work, we investigate electron concentration change in the ionosphere, preceding the onset of strong earthquakes with M ≥ 6.0 in Kamchatka region. The research technique is based on calculating the correlation coefficient between the critical frequency foF2 values at two ionospheric stations. One of them is located inside the earthquake preparation zone, and the other is outside it. The data obtained at two stations PETROPAVLOVSK (PK553) and EARECKSON (EA653) for the period 01.09.2018–30.04.2021 are considered. Statistical analysis of the critical frequencies foF2 showed that a noticeable decrease in the correlation coefficient was observed 1–7 days before the earthquakes for 6 out of 8 events with M ≥ 6.0 that occurred at depths of up to 100 km and epicentral distances of up to 500 km from the PK553 location.

Ionosphere Influenced From Lower-Lying Atmospheric Regions

The ionosphere represents part of the upper atmosphere. Its variability is observed on a wide-scale temporal range from minutes, or even shorter, up to scales of the solar cycle and secular variations of solar energy input. Ionosphere behavior is predominantly determined by solar and geomagnetic forcing. However, the lower-lying atmospheric regions can contribute significantly to the resulting energy budget. The energy transfer between distant atmospheric parts happens due to atmospheric waves that propagate from their source region up to ionospheric heights. Experimental observations show the importance of the involvement of the lower atmosphere in ionospheric variability studies in order to accurately capture small-scale features of the upper atmosphere. In the Part I Coupling, we provide a brief overview of the influence of the lower atmosphere on the ionosphere and summarize the current knowledge. In the Part II Coupling Evidences Within Ionospheric Plasma—Experiments in Midlatitudes, we demonstrate experimental evidence from mid-latitudes, particularly those based on observations by instruments operated by the Institute of Atmospheric Physics, Czech Academy of Sciences. The focus will mainly be on coupling by atmospheric waves.

A new classification of the Arctic spring transition in the middle atmosphere

<p>In the middle atmosphere, spanning the stratosphere and mesosphere, spring transition is the time period where the zonal circulation reverses from winter westerly to summer easterly which has a strong impact on the vertical wave propagation influencing the tropospheric and ionospheric variability. The spring transition can be rapid in form of a final sudden stratospheric warming (SSW, mainly dynamically driven) or slow (mainly radiatively driven) but also intermediate stages can occur. In most studies spring transitions are classified either by their timing of occurrence or by their vertical structure. However, all these studies focus exclusively on the stratosphere and can give only tendencies under which pre-winter conditions an early or late spring transition takes place and how it takes place. Here we classify the spring transitions regarding their vertical-temporal development beginning in January and spanning the whole middle atmosphere in the core region of the polar vortex. This leads to five classes where the timing of the SSW in the preceding winter and a downward propagating Northern Annular Mode (NAM) plays a crucial role. The results show distinctive differences between the five classes in the months before the spring transition especially in the mesosphere allowing a certain prediction for some of the five spring transition classes which would not be possible considering the stratosphere only.</p>

Differences in regular and storm time ionospheric variability at magnetically conjugated locations of the Northern and Southern Hemisphere

<p>The paper is focused on differences/similarities in regular daily ionospheric variability and in the ionospheric response to CME- and CIR/CHSS-related magnetic disturbances above magnetically conjugated ionospheric stations located at Northern and Southern Hemisphere. We analysed variability of critical frequency foF2 and the F2 layer peak height hmF2 obtained for European-African sector for initial, main and recovery phases of magnetic storms of different intensity, which occurred within the last two solar cycles. We also used exclusively GPS-based detection methods, specifically information on TEC, TEC deviations in space and time from a background reference (dTEC), and the Rate of TEC change in time (ROT), all inferred from GPS receiver networks in Europe and Africa to compare behavior of Large Scale Traveling Ionospheric Disturbances (LSTIDs) at both hemispheres. We conclude that hemispheric conjugacy of LSTID is highly probable during both CME- and CIR/CHSS-related storms while interhemispheric circulation rather unlikely but still occurring during some periods.</p>

Study of Ionospheric Variability During Super Substorms

This paper study variability of three ionospheric parameters foF2, h′F and hmF2 to investigate the middle latitude ionospheric effect at Boulder, Colorado, USA (40°N, l105.0° W) during super substorms (SSSs) of 24 August 2005, and 7 September 2017 and 8 September 2017 respectively. Continuous wavelet transform (cwt) implemented to identify the low and high frequency and longer and shorter duration present in the signal. The result shows decrease in foF2 during SSSs of 24 August 2005 and 8 September 2017 and increase in foF2 during 7 September 2017. The highest fluctuation in h′F is noticed during SSS of 24 August 2005. The cwt shows that the coupling between solar wind and magnetosphere occurs between ~ 16 to 32 minutes for SSS of 24 August 2005 and between 27.9 to 64 minutes during super substorm of 7 and 8 September 2017 for all the ionospheric parameters respectively. This study leads to understand the impact of SSSs on communication signals due to energy injected in ionosphere during the coupling mechanism between magnetosphere-ionosphere.

Reply to Comment on Choi et al. Correlation between Ionospheric TEC and the DCB Stability of GNSS Receivers from 2014 to 2016. Remote Sens. 2019, 11, 2657

Choi et al. (2019) suggested that ionospheric total electron content (TEC) and receiver differential code bias (rDCB) stability have a strong correlation during a period of two years from 2014 to 2016. This article is a response to Zhong et al. (2020), who pointed out that the long-term variations of the GPS DCBs are mainly attributed to the satellite replacement rather than the ionospheric variability. In this issue, we investigated the center for orbit determination in Europe (CODE) Global Ionosphere Maps (GIM) products from 2000 to 2020. In this study, changes in TEC and receiver DCB (rDCB) root mean squares (RMS) at Bogota (BOGT) station still have a clear correlation. In addition, there was a moderate correlation between satellite DCB RMS and rDCB RMS. As a result, we suggest that rDCB can be affected simultaneously by GPS sDCB as well as ionospheric activity.

Comment on Choi et al. Correlation between Ionospheric TEC and the DCB Stability of GNSS Receivers from 2014 to 2016. Remote Sens. 2019, 11, 2657

Choi et al. (2019) analyzed the correlation between the ionospheric total electron content (TEC) and the Global Navigation Satellite System (GNSS) receiver differential code bias (DCB) and concluded that the long-term variations of the receiver DCB are caused by the corresponding variations in the ionosphere. Unfortunately, their method is problematic, resulting in conclusions that are not useful. The long-term variations of the Global Positioning System (GPS) DCBs are primarily attributed to the GPS satellite replacement with different satellite block series under the zero-mean constraint condition, rather than the ionospheric variability.

CIR/HSSS-related TID activity and their interhemispheric circulation

<p>The paper presents results of the analysis of the changes in the regular ionospheric variability and TID activity observed during CIR/HSSS-related storms. We analyzed main ionospheric parameters retrieved from manually scaled ionograms, plasma drift measurements and TEC data obtained from several European and African ionospheric stations and GNSS receivers. Most of the observed storm-related TIDs had periods of 60-180 min (LSTIDs). During the analyzed storms we also observed extraordinary spreads and plasma bubbles at the F region heights. The results of the analysis were compared with the TID activity during strong magnetic storms of CME origin along the European-African sector. In order to obtain quantitative information on the likeliness and morphology of interhemispheric circulation of LSTIDs at about 40 events were examined lasting between 8 and 24 hours each. We used exclusively GPS-based detection methods, specifically information on TEC, TEC deviations in space and time from a background reference (dTEC), and the Rate of TEC change in time (ROT), all inferred from GPS receiver networks in Europe and Africa. We conclude that hemispheric conjugacy of LSTID is highly probable while interhemispheric circulation rather unlikely but still occurring during some periods.</p>

Total electron content driven data products of SIMuRG

<p>System for the Ionosphere Monitoring and Researching from GNSS (SIMuRG, see <em>https://simurg.iszf.irk.ru</em>) has been developed in ISTP SB RAS. The system servers as proxy for the RINEX data of global GNSS receivers network. SIMuRG automatically downloads, process and visualize GNSS data. Despite of the system takes routine processing task from the researches, which is valuable by itself, it provides newly developed and improved data products. All data products are based on total electron content (TEC) calculated from RINEX and global ionospheric maps GIM. The first data product is ionospheric variations (TEC variations). The variations are widely used for ionospheric studies, but SIMuRG performs calculation using the filtration that suits TEC data the best way. Before new filtration technique was applied major unphysical artifacts were detected in the data. The artifacts could even prevent from correct interpretation of processing results. The variations together with widely used ROTI index which is also implemented in the system helps to study ionospheric variability. The second data product is newly developed “adjusted TEC”. For that we use GIM to force all TEC series from different site-satellite line-of-sights have one reference level. While the reference level is the same, adjusted TEC leaves all the peculiarities exhibited in different TEC series unaffected. Adjusted TEC broaden ionospheric maps capability near the GNSS stations improving time resolution up to 30 seconds and giving better space resolution. The third data product is implementation of D1 method which calculates ionospheric irregularities motion velocity. D1 shows velocity vector while variations show only amplitude of the irregularity (deviation from the background). D1 calculation is designed in the way that it possible to choose scale of the disturbance to study. It makes possible to study the disturbances of different physical origin. D1 is able to show global ionospheric dynamics and can help detect traveling ionospheric disturbances of various scales. The data described above are attribute by the interactive experimental geometry plots, which might consider as one more data product. The geometry plots might be useful since the TEC data cover area of several thousands kilometers across. The fourth data product is global and regional electron content (GEC and REC), see <em>https://simurg.iszf.irk.ru/gec</em> for reference. SIMuRG provides interactive plots of the GEC and REC. While TEC shows the number of electrons in a given direction (surface density), GEC and REC show amount of a plasma in a volume. GEC is weighted sum of the TEC around the globe, REC – in some geographical region. GEC and REC suits for large scale long-living ionospheric variations studies. Using REC we detect after-storm plasma density change in equatorial ionosphere. There is an option to choose region for REC using geographic and geomagnetic coordinates. We also developed the interface for ionospheric events tracking and submission. We hope to use the events database for machine learning purpose. We hope all above newly developed and improved TEC based data products find application among researches.</p><p>This work was performed under the Russian Science Foundation Grant No. 17-77-20005.</p>

Variability of ionospheric parameters by the Swarm satellites for different solar activity

<p>The use of satellite data allows us to study the variability of ionospheric plasma parameters globally without references to ground stations or receivers in different regions of the Earth. The Swarm mission, which was launched in 2014 and is still operational, allows us to investigate the effects of decreasing solar activity on the ionospheric variability. In our study we use the Swarm in-situ measurements of the electron density and derived parameters. This dataset provides characteristics of the plasma variability along the orbit and gives information on plasma density structures in the ionosphere in terms of their amplitudes, gradients and spatial scales. We analyze the variability of these parameters in the contexts of the northern and southern hemispheres, specific latitudinal regions, and the solar activity level. Understanding of the distribution of such parameters in the context of the solar activity level and selected ionospheric regions can have implications for the development of new satellite instruments and for the accuracy of GNSS precise positioning.</p>