Global Navigation Satellite System Total Electron Content Research Articles

Simultaneous Observation of Equatorial Plasma Bubbles and Traveling Ionospheric Disturbances Over Indonesia Following the 15 January 2022 Tonga Volcano Eruption

AbstractWe report our analysis of multi‐diagnostic ionospheric observations over Indonesia following the 15 January 2022 Tonga volcano eruption. Observation data from the Indonesian global navigation satellite system (GNSS) CORS network, ionosondes, and GNSS ionospheric scintillation and TEC monitor receivers, in conjunction with the Himawari‐8 satellite imagery, were used in the analysis. The Lamb waves from the eruption, traveling at 310 m/s, reached eastern part of Indonesia (5,000 km from Tonga) approximately 4 hr after the eruption. The Lamb waves traversed the Indonesian region for 4 hr and 40 min, around sunset period. As a result, some unseasonal equatorial plasma bubbles (EPBs) occurred over this longitude sector, with an interesting initial development pattern. There was a directional split in the zonal drift velocity of these EPBs, where some EPBs drifted eastward with a velocity of 138.0 6.9 m/s and others westward with a velocity of 39.6 2.0 m/s. At the same time, traveling ionospheric disturbances (TIDs) from the Tonga eruption also propagated over the Indonesian region with a velocity of 434.6 21.7 m/s. In the total electron content (TEC) data, interactions between EPBs and TIDs were observed over the region. There were enhancements in the rate‐of‐TEC index (ROTI) and scintillation index, indicating the presence of ionospheric density irregularities. A turbulent ionospheric F‐layer, due to these EPBs and TIDs, caused either spread‐F echoes or a loss of F‐region traces in the ionograms. An intensification of sporadic‐E layer, lasting for a few hours, was also observed in the ionograms.

Empirical orthogonal function based modelling of ionosphere using Turkish GNSS network

The study investigates ionospheric total electron content (TEC) variation over Turkey from the five selected global navigation satellite system (GNSS) stations situated in diverse parts of Turkey. The geomagnetic indices are used and observed TEC are modeled with the technique known as Empirical orthogonal function (EOF). It is valuable to note that the correlation coefficient between observed GNSS TEC values and EOF TEC values varies from 0.8020 to 0.9394. The root means square error (RMSE) values between observed GNSS TEC values and EOF TEC lie between 3.1665 TECU to 4.4220 TECU. These results show that the EOF model performs quite well in the Turkish region and can present the model TEC variations perfectly. Finally, these GNSS observed and EOF-predicted TEC values along with geomagnetic indices are studied with the tropospheric wind speed. The results showed that both observed and modeled TEC have very low correlations with tropospheric wind speed and do not provide any significant value. Hence, we concluded that the ionospheric region is not affected by tropospheric wind speed. It happens because the tropospheric wind speed is a matter of the lower troposphere and its atmospheric pressure while the ionosphere is far from the earth and depends upon the number of free electrons.

Exploring ionospheric dynamics: a comprehensive analysis of GNSS TEC estimations during the solar phases using linear function model

Abstract The modeling and forecasting of Total Electron Content (TEC) play a major role in influencing signals from satellite-based navigation systems and impact the performance of diverse satellite-dependent technologies. The intensity of solar ionizing radiation and the state of geomagnetic field activity influence the Global Navigation Satellite System (GNSS)-TEC. This paper uses a Linear TEC Function (LTF) climatology model to understand ionospheric behavior under solar and geomagnetic activities that cause variations in the electron distribution of the ionosphere medium. The LTF model integrates representations of solar EUV photon (MgII) and geomagnetic (SYMH) activities, incorporating solar-modulated oscillations (periodic variations) at four seasonal cycles and a linear trend. The LTF model examined the time series of GPS-TEC at a location (geographic 34.95° N, 134.05° E) with a time resolution of 1 h, from 1997 to 2016, covering solar cycles 23 and 24. The Root Mean Square Deviation (RMSD) and correlation coefficient between the GNSS-TEC and model TEC (LTF) was 5.30 TECU and 95 %. The results indicate that solar components, as well as annual and semi-annual variations, have a significant impact on the daily average TEC. Solar activity appears to be the predominant determining factor of TEC during the solar phases of cycles 23 and 24. In contrast, periodic influences primarily outline TEC during periods characterized by minimal solar activity. The geomagnetic component presents an increased influence, particularly during storm periods. The model demonstrates superior performance in Total TEC modeling compared to other state-of-the-art approaches.

3D Traveling Ionospheric Disturbances During the 2022 Hunga Tonga–Hunga Ha’apai Eruption Using GNSS TEC

AbstractThe dual frequency Global Navigation Satellite System (GNSS) observations could determine the total electron content (TEC) in the ionosphere. In this study, GNSS TEC was applied to detect traveling ionospheric disturbances (TIDs) after the eruption of Hunga Tonga–Hunga Ha’apai (HTHH) on 15 January 2022. The eruption caused two types of tsunamis, first is tsunami generated by atmospheric wave (meteo‐tsunami) and second is caused by eruption induces water displacement or tsunami classic. At the same time with former tsunami, the atmospheric wave (shock and lamb waves) also caused TIDs at a speed of approximately ∼0.3 km/s. We found moderate correlation between this TIDs amplitude and the tsunami wave height model from tide gauge stations in New Zealand (0.64) and Australia (0.65). Further we attempted to reveal 3D structure of the TIDs in New Zealand, South Australia, and Philippines using 3D tomography. The tomography was set up > 1,170 blocks, as large as 1.0° (east–west) × 1.0° (north–south) × 100 km (vertical), up to 600 km altitude over selected regions. Tomogram shows beautiful concentric directivity of the first TIDs generated by atmospheric wave (AW).

Indonesian “tsunami-generation” in a nutshell: systematic literature review

Publications related to tsunami-generation phase hold 42.8% of all the publications over all tsunami hazard literatures in Indonesia. Corroborating the fact, tsunami-generation studies are also vital in determining the type of propagation and inundation that may appear in the surrounding coastal regions, which in practice, can also help determining disaster management specifics and risk reduction activities. A literature review regarding tsunami-generation has been done for those purpose by using our CARI! Knowledge Engine, which includes collections of both International and Indonesian journals within the scope of Indonesian locus context. This study unpacks publication trend, the most influential articles, top contributing journals, top main authors, top institutions, and dominant countries as well as the science mapping of tsunami-generation subjects. To gain deeper understanding, we also develop tsunami-generation research framework within the basis of tsunami generation force, time occurrence, monitoring, and measurement approach, as well as hazard analysis approach. From the review, an interesting peak of tsunami-generation publications has been shown in 2020 (39 publications), following the occurrence of several Indonesian major tsunamis in the previous decade and the globally impacting Aceh Tsunami (or the Indian Ocean Earthquake and Tsunami) in 2004. Among all the publications of tsunami-generation force, tsunamigenic earthquake has become the most studied topic (67.7% of all publications), while the most discussed atypical sources mainly come from volcanic eruption (46.67% of all atypical literatures) and submarine landslide (33.33% of all atypical literatures). In terms of tsunami-generation source, it is found that the Sunda Megathrust has been the most-discussed location, which is mentioned within 39 publications, followed by the Palu-Koro Fault with 18 publications and the Krakatau Volcano by 16 publications. Apart from the notice of significant difference between research of historical tsunami and paleotsunami (120:5 publications), some gaps also can be found when discussing the meteotsunami-generation mechanism in Indonesia and the Global Navigation Satellite System’s Total Electron Content (GNSS-TEC) method, which, although proven beneficial for the development of tsunami early warning system, accuracy is still the main issue in capturing tsunami signals and positioning actual source location of tsunami. These were some highlighted limitations that arise in our process to understand tsunami-generation mechanisms and the exploitations of the knowledge. Therefore, future studies specifically within these areas are further encouraged.

Assimilating GNSS TEC with an LETKF over Yunnan, China

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.

Prospects for meteotsunami detection in earth’s atmosphere using GNSS observations

We study, for the first time, the physical coupling and detectability of meteotsunamis in the earth’s atmosphere. We study the June 13, 2013 event off the US East Coast using Global Navigation Satellite System (GNSS) radio occultation (RO) measurements, Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperatures, and ground-based GNSS ionospheric total electron content (TEC) observations. Hypothesizing that meteotsunamis also generate gravity waves (GWs), similar to tsunamigenic earthquakes, we use linear GW theory to trace their dynamic coupling in the atmosphere by comparing theory with observations. We find that RO data exhibit distinct stratospheric GW activity at near-field that is captured by SABER data in the mesosphere with increased vertical wavelength. Ground-based GNSS-TEC data also detect a far-field ionospheric response 9 h later, as expected by GW theory. We conclude that RO measurements could increase understanding of meteotsunamis and how they couple with the earth’s atmosphere, augmenting ground-based GNSS TEC observations.

A Regional Model of Topside Ionospheric Effective Scale Heights Derived From Ionosonde and GNSS TEC

AbstractIonospheric scale height is critical for the structure of electron density profile in the ionosphere. However, mostly, the effective scale heights (fitted with mathematical functions) instead of topside ionospheric scale heights were used to reconstruct the topside profile. In this study, a new method was proposed to estimate topside ionospheric effective scale heights from ionosonde and Global Navigation Satellite System (GNSS) vertical total electron content (TEC). First, the bottomside electron density profile can be estimated from ionograms. Then, combined the parameters of the F2 layer and effective scale heights, topside electron density profile can be estimated by Epstein Layer. Furthermore, ionosonde TEC can be compared with GNSS vertical TEC by adjusting effective scale heights to obtain best‐fit ionosonde TEC and effective scale heights. In this study, ionosonde and GNSS TEC data recorded at Zhangye station (39.40°N, 100.13°E) were used to test the performance. Results show that diurnal and seasonal characteristics of effective scale heights are consistent with previous studies. Furthermore, the Empirical Orthogonal Function technique was used to build a regional and empirical model of effective scale heights. Results show that the accuracy (between ±2.5 TECU) of ionosonde TEC are about 91.26% by comparing with GNSS TEC. Considered that the traditional methods mostly underestimated TEC, it indicates that the accuracy of ionosonde TEC can be improved significantly by the new model of effective scale heights. Furthermore, results show that the topside electron densities estimated by the proposed method are comparable with in situ observations measured by Swarm B.

Multi-instrument study of a spread-F event at Arecibo linked to solar wind variations

Using five diverse data sets, we demonstrate that apparently local ionospheric spread-F activity, observed with an HF radar and the Arecibo Observatory (AO) Incoherent Scatter Radars (ISR) under geomagnetically quiet conditions, is likely the local manifestation of a mesoscale or larger ionospheric response to relatively weak solar wind activity. The solar wind activity included a weak pressure pulse and Interplanetary Magnetic Field (IMF) realignment events. The mid-latitude spread-F activity was observed with a 4.42 MHz radar located near AO and the dual-beam 430 MHz AO ISRs. Additionally, the Coupling, Energetics and Dynamics of Atmospheric Regions (CEDAR) Madrigal Global Navigation Satellite System-Total Electron Content (GNSS-TEC), the NASA OMNI, and the SuperMAG datasets were used to establish the (mesoscale/global) wide-context of the local, deep-context radar results. While the ISR event appears to be “classical” local spread-F, often attributed to Perkins-like plasma instabilities, the HF radar reveals large ionospheric structures coincident with the ISR event. However, that the apparently AO-local event was part of a very dynamic mesoscale event that lasted for over ten hours, is shown via independent AO-sector and AO-conjugate sector keograms constructed using detrended vertical TEC (delta-vTEC) data. The keograms reveal a prominent F-region feature that appears to propagate from west-to-east and then back to the west passing over AO twice. The ISR manifestation of this mesoscale event is indistinguishable from decades of similar ISR results which were assumed to be strictly “local”. The strong non-local (mesoscale) properties revealed by the delta-vTEC keograms suggest a possible space weather influence. Study of the relevant NASA OMNI solar wind and superMAG magnetometer data points to modest solar wind features including IMF realignment that may have electrodynamically launched, with little time delay, the observed mesoscale ionospheric events. Given the apparent rapid ionospheric response to the solar wind features, we suggest that limited latitudinal scale, prompt penetration electric fields (PPEF), associated with highly-localized partial ring current activity, be considered in the system-of-systems context of our observations. In any case, the need to examine magnetospheric/ionospheric electrodynamics across a wide range of instruments and data sets is demonstrated.

Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN

AbstractTimely, reliable and comprehensive global observation information is essential for space weather research. However, limited observation technology hinders the consecutive global coverage of observation data. For the integrity and continuity of the global observation data, deep learning can obtain a global Ionospheric total electron content (TEC) map by fusing multi‐source TEC maps. Different from the previous methods, in the study, a deep learning hybrid model (RFGAN) based on Dual‐Discriminator Conditional Generative Adversarial Network (DDcGAN) and Free‐Form Image Inpainting with Gated Convolution (Deepfill v2) is proposed to fuse the Massachusetts Institute of Technology (MIT)—TEC, International Global Navigation Satellite System TEC (IGS‐TEC) and altimetry satellite TEC. Throughout the RFGAN structure, we use an autoencoder model with gated convolution to inpaint the missing parts of MIT‐TEC and altimetry satellite TEC. Meanwhile, DDcGAN fuses the inpainted MIT‐TEC (MIT'‐TEC) and IGS‐TEC to get a global TEC map with high accuracy. To a certain extent, we inpainted the ocean area of MIT‐TEC through RFGAN. At the same time, RFGAN keeps the consistency of RFGAN‐TEC and MIT‐TEC in the continent area. Our proposed deep learning hybrid model can be easily extended and widely applied to other fields of space science, especially in addressing observational data loss and multi‐source data fusion.

Analysis and Characterization of an Unclassified RFI Affecting Ionospheric Amplitude Scintillation Index Over the Mediterranean Area

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS signal bands, observed and captured through an experimental, software defined radio setup. The paper also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.

Ionospheric responses on the 21 August 2017 solar eclipse by using three-dimensional GNSS tomography

In this study, we have investigated the ionospheric responses on the August 2017 solar eclipse event by using a three-dimensional tomography algorithm with the ground-based GNSS (Global Navigation Satellite System) total electron content observations around Northern America. This three-dimensional ionospheric electron density structure from the tomography can provide us more information regarding the density variations and propagations of disturbances. Results show that the ionospheric electron density depletion triggered by the solar eclipse started from the higher ionosphere and then extended to lower altitudes. The maximum electron density depletion is around 40% compared with the previous day of solar eclipse. After around 30 min of the totality, the electron density continuously returned to the normal level. We further conduct a procedure of Fourier analyses to derive the vertical phase and group velocities of the electron density propagations. Results show that the opposite directions of the vertical phase and group velocities around 220–240 km altitude imply the energy/oscillation source by the solar eclipse.Graphical

Aspects related to variability of radiative cooling by NO in lower thermosphere, TEC and O/N2 correlation, and diffusion of NO into mesosphere during the Halloween storms

Nitric Oxide is a very important trace species which plays a significant role acting as a natural thermostat in Earth’s thermosphere during strong geomagnetic activity. In this paper, we present various aspects related to the variation in the NO Infrared radiative flux (IRF) exiting the thermosphere by utilizing the TIMED/SABER (Thermosphere Ionosphere Mesosphere Energetics and Dynamics/ Sounding of the Atmosphere using Broadband Emission Radiometry) observational data during the Halloween storm which occurred in late October 2003. The Halloween storm comprised of three intense-geomagnetic storms. The variability of NO infrared flux during these storm events and its connection to the strength of the geomagnetic storms were found to be different in contrast to similar super storms. The connection between the quantum of energy outflux from the upper atmosphere into space in terms of NO IRF and the duration of storms is established. The NO radiative cooling, and the closely correlated depletion in O/N2 ratio are controlled by the Joule heating intensity (proxied by AE-index). The collisional excitation rate of NO, calculated using the modelled datasets of WACCM-X (Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension), correlates well with the observed pattern of radiative emission by NO. Observational datasets from TIMED/GUVI (Global Ultra-Violet Imager) and MIT Haystack observatory madrigal GNSS (Global navigation satellite system) total electron content (TEC) database shows that the TEC and O/N2 enhancement in low-mid northern hemispheric latitudes are mainly controlled by the z-component of Interplanetary magnetic field (IMF-Bz). The penetration of eastward electric field during the storm events is found to be responsible for the overall enhancement of TEC. The contribution of enhanced day-side TEC in observed variation of O/N2 ratio by GUVI is also reported. It is also seen that during substorms related events the night-time polar region experiences more cooling due to NO than the daytime polar region. The connections between the mid- and low-latitude enhancement in NO IRF with the propagation of LSTIDs (Large-scale traveling ionospheric disturbances) in combination with the O/N2 variability, and the altitudinal variation in NO flux with the progression of the storm is also investigated. This study presents the evidence on the role of diffusion processes in the large scale enhancement of NO in the mesospheric altitudes.

Ionospheric Assimilation of GNSS TEC into IRI Model Using a Local Ensemble Kalman Filter

Ionospheric total electron content (TEC) is important data for ionospheric morphology, and also an important parameter for ionospheric correction in Global Navigation Satellite System (GNSS) precise positioning, navigation, and radio science. In this study, we present a data assimilation model for regional ionosphere based on a local ensemble Kalman filter (LEnKF) with the International Reference Ionosphere 2016 (IRI-2016) model as the background, to assimilate ionospheric TEC observations from GNSS. To demonstrate the results, the TEC estimates from the Crustal Movement Observation Network of China (CMONOC), which is about 260 stations in China, are applied as observation. The assessments are performed against the TEC estimates from BeiDou Navigation Satellite System (BDS) geostationary earth orbit (GEO) and against the final products from the Center for Orbit Determination in Europe (CODE). The assimilation results are in good agreement with BDS GEO TEC and the CODE TEC on a quiet or disturbed day. The correlation coefficient after assimilation is increased by about 17% compared with that before assimilation, and the RMSE after assimilation is decreased by about 42% compared with that before assimilation. Furthermore, the assimilated method is also evaluated in the single-frequency precise point positioning (PPP). The experimental results indicate that the PPP/Assimilated method can improve the GNSS positioning accuracy more effectively in comparison to the PPP/CODE. These results reveal that the LEnKF method can be considered as a useful tool for ionospheric assimilation.

Ionospheric precursors of strong earthquakes observed using six GNSS stations data during continuous five years (2011–2015)

This study reports the morphological characteristics of anomalous variations in Global Navigation Satellite System Total Electron Content (GNSS-TEC) prior to the strong local earthquakes (EQ) that occurred during the period of 2011–2015. We have analyzed 20 earthquakes of magnitude M ≥ 5.6. A statistical technique is implemented on the data of six GNSS stations located in Tashkent, Kitab, and Maidanak in Uzbekistan, and Islamabad, Multan, Quetta in Pakistan. The results show continuous anomalous variations in TEC during 24 h before the occupancy of local earthquakes. It is shown that the precursors before the occurrence of strong earthquakes, in particular of magnitude 5.7, 7.7, 7.5, 7.8 and 7.3 are detected near Eastern Uzbekistan (26 May 2013), Southwestern Pakistan (24 September 2013), Hindukush region of Afghanistan (26 October 2015), and Central Nepal (25 April 2015) and (12 May 2015), respectively. The ionospheric anomalies appearing before the strong earthquakes at six GNSS stations are registered in 14 cases (70%) out of 20 selected EQs. It is depicted that anomalies referred to as ionospheric precursors appeared about 1–7 days prior to the occurrence of strong earthquakes.

Investigation of the relationship between f0f2 and the TEC using single station neural network models

The relationship between the ionospheric critical plasma frequencies (f0F2) and GNSS-TEC (Global Navigation Satellite System –Total Electron Content) measurements was investigated using an artificial neural network method. About 20 pairs of ionosonde-GNSS receiver stations from 2000 to 2016 were used. Results from this work indicate that the relationship between f0F2 and TEC is mostly affected by the seasons, followed by the level of solar activity, and then the local time. Geomagnetic activity was the least significant of the factors investigated. The relationship between f0F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. Single station models predicted the f0F2 more accurately at their particular localities and clearly overestimated values of the f0F2 ionosonde observations when used at different localities. This finding indicates that model predictions are better (in terms of reduced prediction errors) for the stations for which they are developed than for a different station. Our result visibly point out that models developed for a particular station cannot be effectively applied in another station located farther apart in space. The new approach described in this study represents an important contribution in space weather prediction.

GNSS TEC-Based Earthquake Ionospheric Perturbation Detection Using a Novel Deep Learning Framework

In this article, a new method for seismic ionospheric Global Navigation Satellite System (GNSS) total electron content (TEC) based anomaly detection using a deep learning framework is proposed. The performance of the proposed encoder&#x2013;decoder long short-term memory extended model is compared with those of five other benchmarking predictors. The proposed model achieves the best performance (<i>R</i>² &#x003D; 0.9105 and root-mean-square error (RMSE) &#x003D; 2.6759) in predicting TEC time series data, demonstrating a 20&#x0025; improvement in <i>R</i>² and 39.1&#x0025; improvement in the RMSE over the autoregressive integrated moving average model. To detect the pre-earthquake ionospheric disturbances more accurately, a reasonable strategy for determining anomaly limits is also proposed. Finally, the method is applied to analyze the pre-earthquake ionospheric TEC disturbance of the 2016 Xinjiang <i>M</i>_<i>s</i> 6.2 earthquake. By excluding the effects of solar activity and geomagnetic activity, obvious ionospheric anomalies could be observed, occurring during 4&#x2013;8 days prior to, and on 1 day before, the earthquake. Negative anomalies tended to occur in the earlier period, whereas positive anomalies occurred closer to the earthquake time, with increasing anomaly intensity with temporal proximity. Furthermore, confusion analysis is used in this article to verify the reliability of the proposed model. The proposed model achieves significant improvements in predicting GNSS TEC time series and is shown to advance the performance of earthquake anomaly detection technology.

TEC Map Completion Through a Deep Learning Model: SNP‐GAN

AbstractThe limited availability of ground receiver stations causes an approximate 52% of data gaps in Massachusetts Institute of Technology (MIT)‐ total electron content (TEC) global maps. The completed TEC maps are highly desirable for both scientific research and space weather applications. Compared to the conventional image inpainting methods, the deep learning methods using generative adversarial networks (GANs) offer an effective image inpainting tool. We adapt 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 (IGS‐TEC) maps, as the training data. For 10‐fold cross‐validation of 20‐year IGS‐TEC data, SNP‐GAN reduces the root mean squared error (RMSE) by more than 30% compared to our previous model, the deep convolutional GAN with Poisson blending (DCGAN‐PB). Two case studies using MIT‐TEC data for 2013 and 2016 storms also demonstrate that SNP‐GAN outperforms DCGAN‐PB in terms of recovering equatorial and low latitude TEC structures. Meanwhile, the end‐to‐end styled generator of SNP‐GAN saves time in the map completion step by avoiding iterative mapping used in DCGAN‐PB. Both deep learning methods not only preserve the large‐scale TEC structures well, but also reveal mesoscale (100–1,000 km) TEC structures that are missing in IGS‐TEC. This work represents an important progress for efficient and automatic TEC map completion with high accuracy.

Lithosphere atmosphere ionosphere coupling associated with the 2019 Mw 7.1 California earthquake using GNSS andmultiple satellites.

Global Navigation Satellite System (GNSS)-based Earthquake (EQ) anomalies in the ionosphere and troposphere provide explicit evidences to study the coupling between seismic events, atmosphere, and ionosphere in epicentral breeding regions consequent to theEQ day in the preparation period. EQs are still not predicted, but the space-based EQ anomalies aid in the development of monitoring pre- and post-seismic precursors around the seismogenic zone and associated fault lineament regions. In this paper, tropospheric and ionospheric anomalies are investigated for the July 06, 2019, Mw 7.1 California EQ from GNSS tropospheric delays and Total Electron Content (TEC), respectively. We noticed that atmospheric and ionospheric anomalies from GNSS stations within 5-10days before the main shock and storm-induced ionospheric variations occur beyond the 5th day after the EQ. Similarly, synchronized and collocated lower atmospheric anomalies are also recorded in the long-term temporal values of SO2 and SO4 within 1-month before and after July 2019, which validates the existence of Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) over the EQ epicenter. On the other hand, EQ anomalies occur during quiet geomagnetic storm activity (Kp < 3; Dst < - 20 nT) and geomagnetic storm triggered high-intensity ionospheric variations during Kp > 3. All these atmospheric and ionospheric perturbations support the development in EQ precursors with satellite measurements, which are indispensable towards the forecasting of future EQ.

Ionospheric Response to the December 14, 2020 Total Solar Eclipse in South America

AbstractThe Southern Andes, in Patagonia, are a well‐known hotspot of orographic gravity waves (oGWs) during winter when atmospheric conditions, such as temperature, wind speed, and wind direction, favor their generation and propagation. In the summer, oGWs above the mesosphere and oGW‐induced ionospheric perturbations are rarely observed because vertical wave propagation conditions are unfavorable. Nevertheless, when atmospheric conditions deviate significantly from those typical of summer, for example, during a solar eclipse (SE), the atmospheric temperature and wind changes can allow oGWs to reach ionospheric heights. Global Navigation Satellite Systems (GNSS)‐based ionospheric total electron content (TEC) studies of the 2017 North American eclipse showed oGW‐compatible observations near the totality zone around the Rocky Mountains, and it was suggested, but not shown, that these were likely oGWs. In this work, we report, model, and interpret GNSS TEC perturbations observed during the December 14, 2020 total SE in South America. TEC data recorded near the Andes during this total SE are in good agreement with predictions by the SAMI3 ionospheric model until shortly after the passage of the umbra. TEC data after totality can best be explained with the interpretation that the observation of oGWs was favored by the passage of the eclipse over the Andes Mountains.