Explore the vast range of titles available.

In this paper, the potential extrapolation capabilities and limitations of artificial neural networks (ANNs) are investigated. This is primarily done by generating total electron content (TEC) predictions using the regional southern Africa total electron content prediction (SATECP) model based on the Global Positioning System (GPS) data and ANNs with the aid of multiple inputs intended to enable the software to learn and correlate the relationship between their variations and the target parameter, TEC. TEC values are predicted over regions that were not covered in the model's development, although it is difficult to validate their accuracy in some cases. The SATECP model is also used to forecast hourly TEC variability 1 year ahead in order to assess the forecasting capability of ANNs in generalizing TEC patterns. The developed SATECP model has also been independently validated by ionosonde data and TEC values derived from the adapted University of New Brunswick Ionospheric Mapping Technique (UNB‐IMT) over southern Africa. From the comparison of prediction results with actual GPS data, it is observed that ANNs extrapolate relatively well during quiet periods while the accuracy is low during geomagnetically disturbed conditions. However, ANNs correctly identify both positive and negative storm effects observed in GPS TEC data analyzed within the input space. Key Points Investigation of the extrapolation capability of neural networks Comparison of global IRI model and regional SATECP model Validation of SATECP model with UNB‐IMT derived TEC values

The ionosphere has been considered as one of the major error sources in GNSS signal propagation, and it is still difficult to be modeled precisely, especially for real-time positioning applications. The commonly used ionospheric models are usually based on the one-layer approximation, which neglects the ionospheric variation in the vertical domain and limits the scope of improvement over one-layer models. A new ionospheric model based on the two-layer approximation and two spherical harmonic (SH) functions is proposed in this contribution, where a quasi-globe projection is designed to avoid the inherent ill-posed problem and retain the physical meaning of the SH when regional data are used. GPS and BDS data from the National Positioning Infrastructure of Australia and Crustal Movement Observation Network of China are used for validating the new model’s performance in different areas and different periods. Results show (1) the precision of ionospheric TEC estimates from the new model can be improved by about 26% and 31% in the cross-validation experiment compared to the traditional one-layer model in Australian and Chinese regions, respectively; (2) the positioning accuracy of kinematic single-frequency precise point positioning (SF-PPP) in the experimental regions using the new model reaches about 0.7 m and 0.8 m in the horizontal and vertical components, respectively, in comparison with the one-layer model’s 1.0 m (horizontal) and 1.4 m (vertical); (3) the convergence time of the SF-PPP using the new model is 5–10 min for achieving a sub-meter level of positioning accuracy in both horizontal and vertical components, whereas it needs 30–40 min in case the one-layer model is used.

For the purposes of routinely providing reliable and low-latency Global Ionosphere Maps (GIMs), a method of estimating hourly updated near real-time GIM with a time latency of about 1–2 h based on a 24-h data sliding window of Global Navigation Satellite System (GNSS) near real-time observations and real-time data streams was presented. On the basis of the implementation of near real-time GIM estimation, an hourly updated GIM nowcasting method was further proposed to improve the accurate of short-term total electron content (TEC) prediction. We estimated the Shanghai Astronomical Observatory near real-time GIM (SHUG) and nowcasting GIM (SHPG) in the solar relatively active year (2014) and quiet year (2021), and employed GIMs provided by the International GNSS Service, the Global Positioning System (GPS) differential slant TECs (dSTECs) extracted from global independent GNSS stations, and the vertical TECs (VTECs) inverted from satellite altimetry as the references to validate the estimated results. The GPS dSTECs evaluation results show that SHUG behaves fairly consistent with the rapid GIMs, with a discrepancy of less than 1 TEC unit (TECu) overall. The standard deviations (STDs) of SHUG with respect to Jason-2/-3 VTECs are no more than 10% over the majority of rapid GIMs due to the instability of observations. The performance of 1-h nowcasting SHPG is significantlybetter than the Center for Orbit Determination in Europe (CODE) 1-day predicted GIM (C1PG). GPS dSTEC validation results indicate that 1-h nowcasting SHPG is 1 to 2 TECu more reliable than C1PG in eventful ionospheric electron activity regions, and it outperforms the C1PG by 10% overall versus Jason-2/-3 VTECs. The hourly updated SHUG and SHPG have relatively high reliability and low time latency, and thus can provide excellent service for (near) real-time users and offer more accurate TEC background information than daily predicted GIM for real-time GIM estimation.

Global Positioning System (GPS) allows the detection of ionospheric disturbances associated with the vertical displacements of most of the major shallow seismic events. We describe a method to model the time and space distributions of Rayleigh wave induced total electron content (TEC) patterns detected by a dense GPS array. We highlight the conditions for which a part of the ionospheric pattern can be directly measured, at teleseismic distance and above the epicenter. In particular, a satellite elevation angle lower than 40° is a favorable condition to detect Rayleigh wave induced ionospheric waves. The coupling between the solid Earth and its atmosphere is modeled by computing the normal modes of the solid Earth–atmosphere system. We show the dependency of the coupling efficiency on various atmospheric conditions. By summation of the normal modes we model the atmospheric perturbation triggered by a given earthquake. This shows that a part of the observation is a Rayleigh‐induced radiation pattern and therefore characteristic of the seismic rupture. Through atmosphere‐ionosphere coupling, we model the ionospheric perturbation. After the description of the local geomagnetic field anisotropic effects, we show how the observation geometry is strongly affecting the radiation pattern. This study deals with the related data for two earthquakes with far‐field and near‐field observations using the Japanese GPS network GEONET: after the 12 May 2008 Wenchuan earthquake (China) and after the 25 September 2003 Tokachi‐Oki earthquake (Japan), respectively. Waveforms and patterns are compared with the observed TEC perturbations, providing a new step toward the use of ionospheric data in seismological applications. Key Points We describe how Rayleigh‐wave‐induced ionospheric TEC disturbances are observed We propose a model for the observations at near and far field We compare the synthetics with the GPS‐TEC observations

We present simulation results of the vertical structure of Large Scale Traveling Ionospheric Disturbances (LSTIDs) during synthetic geomagnetic storms. These data are produced using a one‐way coupled SAMI3/Global Ionosphere Thermosphere Model (GITM) model, where GITM provides thermospheric information to SAMI3 (SAMI3 is Another Model of the Ionosphere), producing LSTIDs. We show simulation results which demonstrate that the traveling atmospheric disturbances (TADs) generated in GITM extend to the topside ionosphere in SAMI3 as LSTIDs. The speed and wavelength (600–700 m/s and 10º–20° latitude) are consistent with LSTID observations in storms of similar magnitudes. We demonstrate the LSTIDs reach altitudes beyond the topside ionosphere with amplitudes of <5% over background which will facilitate the use of plasma measurements from the topside ionosphere to supplement measurements from Global Navigation Satellite System in the study of Traveling Ionospheric Disturbances (TIDs). Additionally, we demonstrate the dependence of the characteristics of these TADs and TIDs on longitude. Plain Language Summary Large Scale Traveling Ionospheric Disturbances are a type of wave that occurs in the ionosphere, a layer of the atmosphere dominated by plasma where the motions of particles are highly subject to the magnetic field, during geomagnetic storms. We utilize two models of Earth's atmosphere and ionosphere to show how these waves behave and show that their location, timing, and speed is dependent on various storm characteristics, timing, and location. We also show that a high‐altitude satellite measuring plasma density in the ionosphere should be able to detect the characteristics of these waves. Key Points We demonstrate that traveling ionospheric disturbances can be produced in simulations of the ionosphere‐thermosphere system We show that these traveling ionospheric disturbances extend to the topside ionosphere in simulations

As a new Ionosphere Associate Analysis Center (IAAC) of the International GNSS Service (IGS), Chinese Academy of Sciences (CAS) started the routine computation of the real-time, rapid, and final Global Ionospheric Maps (GIMs) in 2015. The method for the generation of CAS rapid and final GIMs and recent updates are presented in the paper. The quality of CAS post-processed GIMs is assessed during 2015–2018 after the maximum of solar cycle 24. To perform an independent and fair assessment, Jason-2/3 Vertical Total Electron Contents (VTEC) are first used as the references over the ocean. GPS differential Slant TECs (dSTEC) generated from 55 Multi-GNSS Experimental (MGEX) stations of the IGS are also employed, which provides a complementing way to evaluate the ability of electron content models to reproduce the spatial and temporal gradients in the ionosphere. During the test period, Jet Propulsion Laboratory (JPL) GIMs present significantly positive deviations compared to the Jason VTEC and GPS dSTEC. Technical University of Catalonia (UPC) rapid GIM UQRG exhibits the best performance in both Jason VTEC and GPS dSTEC analysis. The CAS GIMs show comparable performance with the results of the first four IAACs of the IGS. As expected, the poor performance of all GIMs is in equatorial regions and the high latitudes of the southern hemisphere. The consideration of generating multi-layer or three-dimensional ionospheric maps is emphasized to mitigate the inadequacy of ionospheric single-layer assumption in the presence of pronounced latitudinal gradients. The use of ionospheric observations from the new GNSS constellations and other space- or ground-based observation techniques is also suggested in the generation of future GIMs, given the sparse GPS/GLONASS stations in the southern hemisphere.

Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ −100 nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to 32.1% across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning.

This study explores machine learning models to gain insights into dynamics of ionospheric irregularities over geodetic receivers in Mbarara (0.60° S, 30.74° E) and Kigali (1.94° S, 30.09° E). A seven-year rate of total electron content index (ROTI) database and two modeling approaches (multivariate and univariate) were employed. The motivation was to treat the database with time series techniques following a case study with and without the influence of solar wind parameters. The objective is to examine how each approach reconstructs the morphology of ROTI within 3-h time steps over a 24-h cycle. To achieve this, five machine learning models, including extreme gradient boosting (XGBoost), random forest (RF), bidirectional long-short term memory (BLSTM), unidirectional long-short term memory (LSTM) and nonlinear autoregressive with eXogenous input (NARX), were developed and evaluated. Test results demonstrate significant performance variations highlighting comparable ROTI reconstructions in the absence of the solar wind features. The RF model exhibited superior performance with the lowest mean absolute errors of 0.03 and 0.07 TECU/min and accuracies of 93% and 75% under multivariate and univariate modeling, respectively. Based on the RF model’s performance, we employed an extended database over the Ugandan (Mbar) station for further model development and validated its efficiency over a station in Rwanda (Nurk). The results provided promising insights, emphasizing the need for future research dedicated to robust and enhanced nowcasting models that leverage long-term ionospheric data, especially in regions with limited scintillation monitors.

Hexagonal distributed embedded energy converters, also known as hexDEECs, are centimeter-scale energy transducers that leverage variable capacitance to generate electricity when their hyperelastic structure is dynamically deformed. To better understand, characterize, and optimize hexDEEC designs, a series of numerical methods and techniques were developed to model the hyperelastic mechanics of hexDEECs, electrostatic properties, and electricity generation characteristics. The numerical methods developed for the hyperelastic structural analysis were corroborated by empirical results from another study, and the models and equations for capacitance, electrostatic forces, and electrical potential energy were derived from fundamental electrostatic equations. These methods and techniques were implemented within the STAR-CCM+ multiphysics software Version 2020.3 (15.06.008) environment. Results from this analysis revealed methodologies and techniques necessary to model the energy converters, which will enable future exploration and optimization of more specific designs and corresponding applications.

The Bézier curves submitted by Pierre Bézier in the mid-1950s are splendid differential geometric structures. The paper compares a mechanical–theoretical curve (classical-conventional) with a computer-based artificial neural network (modern). While the reader witnesses hourly TEC (TECU) modeling by the class C 0 Bézier structure for the first time, (s)he has the opportunity to evaluate the reliable results of segmented-continuous curves with network. The Bézier structure is established by operating the differential geometrical invariants. The yielding and accuracy of the models are evaluated with the R correlation coefficient and the mean squared error . The discussion demonstrates the harmony of the two different approaches by modeling the 365-day TEC map of 2017. Then, in particular, the outputs of the intense geomagnetic storms of 28 May (Dst = − 125 nT) and 8 September (Dst = − 122 nT) are modeled and compared. Bézier curves are read segmental-continuous every 12-h TEC map in all discussions. The network, on the other hand, employs solar wind parameters to model the TEC atlas. The models obey rigorously the causality principle. The results of the discussion display that the R score reaches around 93% and 98.8% for Bézier curves and the neural network, respectively. Again, it is noticed that the mean squared error of the network model decreases to 1.1308 TECU. The curve model emerges to the reader as an alternative to neural networks in projections of space climate conditions.