Showing papers on "TEC published in 2021"
TL;DR: A framework of edge computing-enabled SAGINs to support various Internet of Vehicles (EC-IoV) services for the vehicles in remote areas is proposed and a preclassification scheme to reduce the size of action space and a deep imitation learning-driven offloading and caching algorithm is proposed to achieve real-time decision making.
Abstract: Edge computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space-air-ground integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled space-air-ground integrated networks (EC-SAGINs) to support various IoV services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a pre-classification scheme is presented to reduce the size of action space, and a deep imitation learning (DIL) driven offloading and caching algorithm is proposed to achieve real-time decision making. Simulation results show the effectiveness of our proposed scheme. At last, we also discuss some technology challenges and future directions.
72 citations
TL;DR: This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals.
Abstract: Prominent advances in the field of artificial intelligence during the past decade and the breakthrough of deep learning would be useful for investigating ionospheric weather using ground and space-based ionospheric sensors data. The significance of deep learning algorithms needs to be assessed in forecasting the low latitude ionospheric disturbances (delays) for the global positioning system (GPS) signals. Total electron content (TEC) data sets prepared by taking advantage of GPS satellite radio frequency (RF) signals. This letter provides the application of deep learning models, long short-term memory (LSTM), gated recurrent unit (GRU), and a hybrid model that consists of LSTM combined with convolution neural network (CNN) to forecast the ionospheric delays for GPS signals. The deep learning models implemented using the vertical TEC (VTEC) time-series data estimated from GPS measurements over Bengaluru, Guntur, and Lucknow GPS stations. The LSTM-CNN model performs well when compared to other ionospheric deep learning forecasting algorithms with minimum root-mean-square error (RMSE) of 1.5 TEC units (TECUs) and a high degree of $R^{2} = 0.99$ .
47 citations
TL;DR: The study shows that the deep learning model based on an image translation method will be effective for forecasting of future images using previous data and shows better performance than 1‐day CODE prediction model during both solar maximum and minimum periods.
28 citations
TL;DR: In this paper, the performance and precision of ionospheric observables extracted from different algorithms were investigated using two validation methods, i.e., the co-location experiment by calculating the single difference for each satellite, and the single-frequency PPP (SF-PPP) test by two co-located stations; and use the short arc experiment to demonstrate the advantages of the PPP-fixed method.
Abstract: Precise extraction of ionospheric total electron content (TEC) observations with high precision is the precondition for establishing high-precision ionospheric TEC models. Nowadays, there are several ways to extract TEC observations, e.g., raw-code method (Raw-C), phase-leveled code method (PL-C), and undifferenced and uncombined precise point positioning method (UD-PPP); however, their accuracy is affected by multipath and noise. Considering the limitations of the three traditional methods, we try directly to use the phase observation based on zero-difference integer ambiguity to extract ionospheric observations, namely, PPP-Fixed method. The main goal of this work is to: 1) deduce the expression of ionospheric observables of these four extraction methods in a mathematical formula, especially the satellite and receiver hardware delays; 2) investigate the performance and precision of ionospheric observables extracted from different algorithms using two validation methods, i.e., the co-location experiment by calculating the single difference for each satellite, and the single-frequency PPP (SF-PPP) test by two co-location stations; and 3) use the short arc experiment to demonstrate the advantages of the PPP-Fixed method. The results show that single-difference mean errors of TEC extracted by PL-C, UD-PPP, and PPP-Fixed are 1.81, 0.59, and 0.15 TEC unit (TECU), respectively, and their corresponding maximum single-difference values are 5.12, 1.68, and 0.43 TECU, respectively. Compared with PL-C, the precision of the TEC observations extracted by the PPP-Fixed method is improved by 91.7%, while it is 67.3% for UD-PPP. The SF-PPP experiment shows that PPP-Fixed is the best among these methods in terms of convergence speed, correction accuracy, and reliability of positioning performance. Moreover, the PPP-Fixed method can achieve high accuracy even when the observed arc is short, e.g., within 40 min.
25 citations
TL;DR: In this article, Singular Spectrum Analysis (SSA) was used to extract information from the noisy time-series data with the first four SSA modes in the decomposition, almost converging to total variance of about 99%.
23 citations
TL;DR: A blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs is developed and results show that the scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
Abstract: The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this paper, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.
23 citations
TL;DR: In this paper, all the outputs obtained during the GNSS data processing aiming to explore the relationship between the power of volcanic explosion and ionospheric TEC perturbation are presented.
Abstract: This dataset contains all the outputs obtained during the GNSS data processing aiming to explore the relationship between the power of volcanic explosion and ionospheric TEC perturbation. This dataset also contain the codes used to process the data
22 citations
22 citations
TL;DR: In this paper, support vector machine (SVM) with GPU acceleration was used for developing a regional forecast model for the ionospheric total electron content (TEC) over China region.
22 citations
TL;DR: In this paper, the authors present a comprehensive analysis of pre-seismic anomalies as computed from the ground and space-based techniques during the recent Samos earthquake in Greece on 30 October 2020, with a magnitude M = 6.9.
Abstract: We present a comprehensive analysis of pre-seismic anomalies as computed from the ground and space-based techniques during the recent Samos earthquake in Greece on 30 October 2020, with a magnitude M = 6.9. We proceed with a multi-parametric approach where pre-seismic irregularities are investigated in the stratosphere, ionosphere, and magnetosphere. We use the convenient methods of acoustics and electromagnetic channels of the Lithosphere–Atmosphere–Ionosphere-Coupling (LAIC) mechanism by investigating the Atmospheric Gravity Wave (AGW), magnetic field, electron density, Total Electron Content (TEC), and the energetic particle precipitation in the inner radiation belt. We incorporate two ground-based IGS GPS stations DYNG (Greece) and IZMI (Turkey) for computing the TEC and observed a significant enhancement in daily TEC variation around one week before the earthquake. For the space-based observation, we use multiple parameters as recorded from Low Earth Orbit (LEO) satellites. For the AGW, we use the SABER/TIMED satellite data and compute the potential energy of stratospheric AGW by using the atmospheric temperature profile. It is found that the maximum potential energy of such AGW is observed around six days before the earthquake. Similar AGW is also observed by the method of wavelet analysis in the fluctuation in TEC values. We observe significant energetic particle precipitation in the inner radiation belt over the earthquake epicenter due to the conventional concept of an ionospheric-magnetospheric coupling mechanism by using an NOAA satellite. We first eliminate the particle count rate (CR) due to possible geomagnetic storms and South Atlantic Anomaly (SAA) by the proper choice of magnetic field B values. After the removal of the statistical background CRs, we observe a significant enhancement of CR four and ten days before the mainshock. We use Swarm satellite outcomes to check the magnetic field and electron density profile over a region of earthquake preparation. We observe a significant enhancement in electron density one day before the earthquake. The parameters studied here show an overall pre-seismic anomaly from a duration of ten days to one day before the earthquake.
21 citations
TL;DR: In this paper, a fluid-thermal-electric multiphysics coupled model is developed to uncover the performance enhancements of both TEG and TEC systems using nanofluids, by which the thermodynamic and thermoelectric performances associated with the nanof-luid flow behaviors can be taken into account simultaneously.
TL;DR: In this article, the accuracy and consistency of the IAAC GIMs during high (2014) and low (2018) solar activity periods of the 24th solar cycle were evaluated.
Abstract: Ionosphere Associate Analysis Centers (IAACs) of the International GNSS Service (IGS) independently produce global ionosphere maps (GIMs) of the total electron content (TEC). The GIMs are based on different modeling techniques, resulting in different TEC levels and accuracies. In this study, we evaluated the accuracy and consistency of the IAAC GIMs during high (2014) and low (2018) solar activity periods of the 24th solar cycle. In our study, we applied two different evaluation methods. First, we carried out a comparison of the GIM-derived slant TEC (STEC) with carrier phase geometry-free combination of GNSS signals obtained from 25 globally distributed stations. Second, vertical TEC (VTEC) from GIMs was compared to altimetry-derived VTEC obtained from the Jason-2 and Jason-3 satellites and complemented for plasmaspheric TEC. The analyzed GIMs obtained STEC RMS values reaching from 1.98 to 3.00 TECU and from 0.96 to 1.29 TECU during 2014 and 2018, respectively. The comparison to altimetry data resulted in VTEC STD values that varied from 3.61 to 5.97 TECU and from 1.92 to 2.78 TECU during 2014 and 2018, respectively. The results show that among the IAACs, the Center for Orbit Determination in Europe global maps performed best in low and high solar activity periods. However, the highest accuracy was obtained by a non-IGS product—UQRG GIMs provided by Universitat Politecnica de Catalunya. It was also shown that the best results were obtained using a modified single layer model mapping function and that the map time interval has a relatively small influence on the resulting map accuracy.
TL;DR: In this article, a heat sink thermoelectric generator consisting of an input system (TEC) that produces a hot and cold liquid and an output system that generates output voltage using the TEC model TEC1-12710.
TL;DR: In this paper, total electron content (TEC) data obtained from a Global Positioning System (GPS) receiver network in Japan during 22 years from 1998 to 2019 were analyzed.
Abstract: In order to reveal solar activity dependence of the medium-scale traveling ionospheric disturbances (MSTIDs) at midlatitudes, total electron content (TEC) data obtained from a Global Positioning System (GPS) receiver network in Japan during 22 years from 1998 to 2019 were analyzed. We have calculated the detrended TEC by subtracting the 1-h running average from the original TEC data for each satellite and receiver pair, and made two-dimensional TEC maps of the detrended TEC with a spatial resolution of 0.15° × 0.15° in longitude and latitude. We have investigated MSTID activity, defined as $$\delta I/\overline{I}$$
, where $$\delta I$$
and $$\overline{I}$$
are standard deviation of the detrended TEC and the average vertical TEC within the area of 133.0°–137.0° E and 33.0°–37.0° N for 1 h, respectively. From each 2-h time series of the detrended TEC data within the same area as the MSTID activity, auto-correlation functions (ACFs) of the detrended TEC were calculated to estimate the horizontal propagation velocity and direction of the MSTIDs. Statistical results of the MSTID activity and propagation direction of MSTIDs were consistent with previous studies and support the idea that daytime MSTIDs could be caused by atmospheric gravity waves, and that nighttime MSTIDs were caused by electro-dynamical forces, such as the Perkins instability. From the current long-term observations, we have found that the nighttime MSTID activity and occurrence rate increased with decreasing solar activity. For the daytime MSTID, the occurrence rate increased with decreasing solar activity, whereas the MSTID activity did not show distinct solar activity dependence. These results suggest that the secondary gravity waves generated by dissipation of the primary gravity waves propagating from below increase under low solar activity conditions. The mean horizontal phase velocity of the MSTIDs during nighttime did not show a distinct solar activity dependence, whereas that during daytime showed an anticorrelation with solar activity. The horizontal phase velocity of the daytime MSTIDs was widely distributed from 40 to 180 m/s under high solar activity conditions, whereas it ranged between 80 and 200 m/s, with a maximum occurrence at 130 m/s under low solar activity conditions, suggesting that gravity waves with low phase velocity could be dissipated by high viscosity in the thermosphere under low solar activity conditions.
TL;DR: In this paper, a support vector machine (SVM) was used for classifying subdaily and diurnal total electron content (TEC) spatial changes prior to solar flare events, in order to assess the possibility of predicting B, C, M, and X-class solar flares events.
Abstract: Predicting where and when space weather events such as solar flares and X-rays bursts are likely to occur in a specific area of interest constitutes a significant challenge in space weather research. Space weather scientists are, therefore, gradually exploring multivariate data analysis techniques from the fields of data mining or machine learning in order to approximate future occurrences of space weather events from past distribution patterns. As solar flares emit extreme ultraviolet and X-ray radiation, which leads to ionization effect in different layers of the ionosphere, most recent works related to solar flare predictions using machine learning (ML) techniques, focused on X-ray time series predictions. Here, we suggest using support vector machine for classifying subdaily and diurnal total electron content (TEC) spatial changes prior to solar flare events, in order to assess the possibility of predicting B, C, M, and X-class solar flare events. This is done as opposed to predicting TEC time series using ML techniques. The predictions are estimated up to three days before each tested class events, along with different skill scores such as precision, recall, Heidke skill score (HSS), accuracy, and true skill statistics. The results indicate that the suggested approach has the ability to predict solar flare events of X and M-class 24 h prior to their occurrence with 91% and 76% HSS skill scores, respectively, which improves over most recent related works. However, for the small-size C and B-class flares, the suggested approach does not succeed in producing similar promising results.
TL;DR: In this paper, the root-mean-square (RMS) maps are used as the accuracy indicator of global ionospheric maps (GIMs) to optimize the stochastic model of precise point positioning algorithms.
Abstract: Aside from the ionospheric total electron content (TEC) information, root-mean-square (RMS) maps are also provided as the standard deviations of the corresponding TEC errors in global ionospheric maps (GIMs). As the RMS maps are commonly used as the accuracy indicator of GIMs to optimize the stochastic model of precise point positioning algorithms, it is of crucial importance to investigate the reliability of RMS maps involved in GIMs of different Ionospheric Associated Analysis Centers (IAACs) of the International GNSS Service (IGS), i.e., the integrity of GIMs. We indirectly analyzed the reliability of RMS maps by comparing the actual error of the differential STEC (dSTEC) with the RMS of the dSTEC derived from the RMS maps. With this method, the integrity of seven rapid IGS GIMs (UQRG, CORG, JPRG, WHRG, EHRG, EMRG, and IGRG) and six final GIMs (UPCG, CODG, JPLG, WHUG, ESAG and IGSG) was examined under the maximum and minimum solar activity conditions as well as the geomagnetic storm period. The results reveal that the reliability of the RMS maps is significantly different for the GIMs from different IAACs. Among these GIMs, the values in the RMS maps of UQRG are large, which can be used as ionospheric protection level, while the RMS values in EHRG and ESAG are significantly lower than the realistic RMS. The rapid and final GIMs from CODE, JPL and WHU provide quite reasonable RMS maps. The bounding performance of RMS maps can be influenced by the location of the stations, while the influence of solar activity and the geomagnetic storm is not obvious.
TL;DR: In this article, the effect of the dimensions of single-slope solar still with TEC on water production rate is simulated using the numerical solution and humid air method, using the simulation is steady, incompressible, and laminar.
TL;DR: In this article, the authors analyzed the transient variations of four different physical parameters in the atmosphere/ionosphere during the M7.8 and M 7.3 earthquakes in Nepal, namely, thermodynamic proprieties in the lower atmosphere; outgoing earth radiation (OLR) at the TOA; GPS/TEC and very-low-frequency (VLF/LF) signals at the receiving stations in Bishkek (Kyrgyzstan) and Varanasi (India).
Abstract: We analyze retrospectively/prospectively the transient variations of four different physical parameters in the atmosphere/ionosphere during the M7.8 and M7.3 earthquakes in Nepal, namely: (i) thermodynamic proprieties in the lower atmosphere; (ii) outgoing earth radiation (OLR) at the TOA; (iii) GPS/TEC and (iv) the very-low-frequency (VLF/LF) signals at the receiving stations in Bishkek (Kyrgyzstan) and Varanasi (India). We found that in mid-March 2015, there was a rapid increase in the increase in radiation from the atmosphere observed by satellites. This anomaly was located close to the future M7.8 epicenter and reached a maximum on April 21-22. The GPS/TEC data analysis indicated an increase and variation in electron density, reaching a maximum value during April 22-24. A strong negative TEC anomaly in the crest of EIA (Equatorial Ionospheric Anomaly) occurred on April 21, and a strong positive anomaly was recorded on April 24, 2015. The behavior of VLF-LF waves along NWC-Bishkek and JJY-Varanasi paths has shown abnormal behavior during April 21-23, several days before the first, stronger earthquake. Our continuous satellite OLR analysis revealed this new strong anomaly on May 3, which was why we anticipated another significant event in the area. On May 12, 2015, an M7.3 occurred. Our results show coherence between the appearance of these pre-earthquake transient’s effects in the atmosphere and ionosphere (with a short time-lag, from hours up to a few days) and the occurrence of the 2015 M7.8 and M7.3 events in Nepal. The spatial characteristics of the pre-earthquake anomalies were associated with a large area but inside the preparation region estimated by Dobrovolsky-Bowman. The pre-earthquake nature of the signals in the atmosphere and ionosphere was revealed by simultaneous analysis of satellite, GPS/TEC, and VLF/LF observations and suggest that they follow a general temporal-spatial evolution pattern been seen in other large earthquakes worldwide.
TL;DR: In this article, the authors investigate abnormal ionospheric signatures before three large magnitudes (Mw > 6.5) and shallow hypocentral depth 3; Dst 3.
TL;DR: The accuracy of the F7/C2 GPS absolute TEC observations is less than 3.0 TEC units as discussed by the authors, which is the best known accuracy for GPS absolute total electron content (TEC) observations.
Abstract: Slant absolute total electron content (TEC) is observed by the Formosa Satellite-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (FORMOSAT-7/COSMIC-2, F7/C2) Tri-GNSS Radio Occultation System (TGRS) instrument. We present details of the data processing algorithms, validation, and error assessment for the F7/C2 global positioning system (GPS) absolute TEC observations. The data processing includes estimation and application of solar panel dependent pseudorange multipath maps, phase to pseudorange leveling, and estimation of separate L1C-L2C and L1C-L2P receiver differential code biases. We additionally perform a validation of the F7/C2 GPS absolute TEC observations through comparison with colocated, independent, TEC observations from the Swarm-B satellite. Based on this comparison, we conclude that the accuracy of the F7/C2 GPS absolute TEC observations is less than 3.0 TEC units. Results are also presented that illustrate the suitability of the F7/C2 GPS absolute TEC observations for studying the climatology and variability of the topside ionosphere and plasmasphere (i.e., altitudes above the F7/C2 orbit of ∼550 km). These results demonstrate that F7/C2 provides high quality GPS absolute TEC observations that can be used for ionosphere-thermosphere data assimilation as well as scientific studies of the topside ionosphere and plasmasphere.
TL;DR: In this article, the performance analysis of thermionic power generation when the interelectrode vacuum gap shrinks to the submicron range was presented, where the Schottky barrier lowering due to image charge perturbation and near-field enhanced radiative heat transfer significantly affects the TEC performance.
Abstract: This paper presents the comprehensive performance analysis of thermionic power generation when the interelectrode vacuum gap shrinks to the submicron range. Although reducing the vacuum gap has been suggested as an effective approach to mitigate space-charge accumulation in thermionic-energy-conversion (TEC) devices, previous theoretical works have predicted the optimal gap distance in the single-digit micrometer range. However, we demonstrate that nanoscale charge and thermal interactions between thermionic electrodes, such as Schottky barrier lowering due to image charge perturbation and near-field enhanced radiative heat transfer, significantly affects the TEC performance within the submicron vacuum gap. Carefully conducted energy-balance analysis reveals that submicron-gap TEC at $d\ensuremath{\approx}700$ nm can produce an approximately fourfold increase in power output with a higher energy conversion efficiency than micron-gap TEC under the same operating condition. In addition, significant thermionic and near-field radiative heating of the collector in the submicron-gap TEC system can be beneficially used to further enhance the power output and efficiency by combining with a bottom-cycle heat engine. We believe that the present work provides a theoretical framework for submicron-gap thermionic power generation as a promising energy recycling scheme for high-quality heat sources.
TL;DR: In this paper, the prediction model for ionospheric total electron content (TEC) based on Long Short-Term Memory (LSTM) deep learning network and its performance are discussed.
Abstract: In this paper, the prediction model for ionospheric total electron content (TEC) based on Long Short-Term Memory (LSTM) deep learning network and its performance are discussed. The input parameters of the model are previous values of daily TEC, solar radio flux at 10.7 cm parameter of 81 day moving average (
$\overline{F107\_81}$
), sunspot number (SSN), geomagnetic Kp index, and disturbance storm time (Dst) index, and the outputs are TEC values for the target day. TEC data from January 1, 2001 to December 31, 2016 were used in this study. The dataset almost covers most of the years of the last two solar cycles (23, 24), and it is separated as 81.3% for training, 6.2% for validation, and 12.5% for testing. At BJFS IGS station (39.61° N, 115.89° E), LSTM yielded good TEC estimates with an RMSE of 4.07 TECU in 2001, it was 33% and 48% lower than the RMSE observed in TEC prediction using BP and IRI-2016 models, respectively. In the year of low solar activity (2016), the RMSE predicted by LSTM was 1.78 TECU, it provided 30% and 54% lower RMSE for TEC prediction than for BP and IRI-2016 models. Under the condition of magnetic storm, the LSTM TEC predictions are more consistent with the corresponding IGS Global Ionospheric Maps (GIMs) TEC than TEC predictions by BP and IRI-2016 models. LSTM can better grasp the influence of different external conditions on TEC. Seventeen grid points along 120° E meridian in latitude range from 80° S to 80° N were selected to further study the performance of LSTM model in different latitude. Results show that the prediction accuracy of LSTM is better than that of BP at different latitudes, especially at low latitudes. The performances of the two models are highly correlated with latitude and solar activity, and are both better than that of IRI-2016.
TL;DR: In this article, a high-latitude ionospheric scintillation model based on geodetic receivers was developed to validate the performance of ROTI statistically, and the data was evaluated against four parameters: 1, the detected daily SCI occurrence rate; 2, the ability to detect the daily occurrence pattern of ICI; 3, the correlation between the detected SCI and the space weather parameters, including the 10.7 cm solar flux, Ap, the H component of longitudinally asymmetric and polar cap north indices; 4, the overall distribution of the
TL;DR: In this paper, the authors investigated ionospheric anomalies associated with the December 19, 2009, Taiwan EQ (Mw 6.4), that occurred on UT 14:02, 13:02h.
Abstract: The recent advances in satellite based ionosphere monitoring provided more evidence about earthquake (EQ) precursors from lithosphere ionosphere coupling mechanism. In this paper, we investigate ionospheric anomalies associated with the December 19, 2009, Taiwan EQ (Mw = 6.4), that occurred on UT = 13:02 h. Seismo ionospheric anomalies are evaluated during 31 days (20 day before followed by 10 days after the EQ) in the data of French Satellite Detection of Electro-Magnetic Emissions Transmitted from Earthquake Regions (DEMETER) and validated from Global Navigation Satellite System (GNSS) retrieved Total Electron Content (TEC). The day time values of ISL (Langmuir Probe) and IAP (Ion Analyzer) from DEMETER point to significant ionospheric perturbations within 5 days associated with EQ day. Moreover, GNSS stations of International GNSS Service (IGS) within EQ preparation zone also validate anomalies in ionosphere prior/after the Mw 6.4, Taiwan EQ. Moreover, spatial and temporal analyses of daily ionospheric indices from DEMETER and GNSS point to simultaneous enhancement in electron density, ion density, electron temperature and TEC related to the main shock during quiet geomagnetic storm (Dst < − 30 nT, Kp ≤ 3). All these evidence aids in promoting the lithosphere- ionosphere coupling over the epicenter of Mw 6.4 during the preparation period from DEMETER and GNSS TEC.
TL;DR: In this paper, a real-time method based on the Total Variometric Approach (TVA) was proposed to include the GNSS realtime data stream in future warning systems and tsunami genesis estimation observing both ground motion and total electron content (TEC) perturbations following seismic events.
Abstract: Global Navigation Satellite System (GNSS) is used in seismology to study the ground displacements as well as to monitor the ionospheric total electron content (TEC) perturbations following seismic events The aim of this work is to combine these two observations in one real-time method based on the Total Variometric Approach (TVA) to include the GNSS real-time data stream in future warning systems and tsunami genesis estimation observing both, ground motion and TEC Our TVA couples together the Variometric Approach for Displacement Analysis Stand-alone Engine (VADASE) with the Variometric Approach for Real-Time Ionosphere Observation (VARION) algorithms We apply the TVA to the Mw 83 Illapel earthquake, that occurred in Chile on September 16, 2015, and we demonstrate the coherence of the earthquake ground shaking and the TEC perturbation by using the same GNSS data stream in a real-time scenario Nominally, we also highlight a stronger kinetic energy released in the north of the epicenter and visible in both, the ground motion and the TEC perturbation detect at 30 s and around 95 min after the rupture respectively The high spatial resolution of ionospheric TEC measurement seems to match with the extent of the seismic source The GNSS data stream by TVA of both the ground and ionospheric measurement opens today new perspectives to real-time warning systems for tsunami genesis estimation
TL;DR: The result shows that the proposed NN model can predict the diurnal and seasonal GPS TEC more accurate than IRI over anomaly crest region Bhopal, and the inclusion of IRI-NmF2 as input layer neuron increases the network performance.
TL;DR: This work adapts the Spectrally Normalized Patch GAN (SNP‐GAN) for the TEC map completion using a traditional complete TEC data source, the International Global Navigation Satellite System TEC maps, as the training data and shows that SNP‐GAN outperforms DCGAN‐PB in terms of recovering equatorial and low latitude TEC structures.
TL;DR: Li et al. as discussed by the authors focused on two geomagnetic storms that happened on 7-8 September 2017 and 25-26 August 2018, which showed the prominent daytime TEC enhancements in the Asian sector during their recovery phases, to explore the nighttime large-scale ionospheric responses as well as the small-scale Equatorial Plasma Irregularities (EPIs).
Abstract: Recent studies revealed that the long-lasting daytime ionospheric enhancements of Total Electron Content (TEC) were sometimes observed in the Asian sector during the recovery phase of geomagnetic storms (e.g., Lei (J Geophys Res Space Phys 123: 3217–3232, 2018), Li (J Geophys Res Space Phys 125: e2020JA028238, 2020). However, they focused only on the dayside ionosphere, and no dedicated studies have been performed to investigate the nighttime ionospheric behavior during such kinds of storm recovery phases. In this study, we focused on two geomagnetic storms that happened on 7–8 September 2017 and 25–26 August 2018, which showed the prominent daytime TEC enhancements in the Asian sector during their recovery phases, to explore the nighttime large-scale ionospheric responses as well as the small-scale Equatorial Plasma Irregularities (EPIs). It is found that during the September 2017 storm recovery phase, the nighttime ionosphere in the American sector is largely depressed, which is similar to the daytime ionospheric response in the same longitude sector; while in the Asian sector, only a small TEC increase is observed at nighttime, which is much weaker than the prominent daytime TEC enhancement in this longitude sector. During the recovery phase of the August 2018 storm, a slight TEC increase is observed on the night side at all longitudes, which is also weaker than the prominent daytime TEC enhancement. For the small-scale EPIs, they are enhanced and extended to higher latitudes during the main phase of both storms. However, during the recovery phases of the first storm, the EPIs are largely enhanced and suppressed in the Asian and American sectors, respectively, while no prominent nighttime EPIs are observed during the second storm recovery phase. The clear north–south asymmetry of equatorial ionization anomaly crests during the second storm should be responsible for the suppression of EPIs during this storm. In addition, our results also suggest that the dusk side ionospheric response could be affected by the daytime ionospheric plasma density/TEC variations during the recovery phase of geomagnetic storms, which further modulates the vertical plasma drift and plasma gradient. As a result, the growth rate of post-sunset EPIs will be enhanced or inhibited.