Total electron content (TEC) is an important parameter for studying ionospheric variations and space weather. Short-term prediction of the ionosphere plays a significant role in near-Earth space environment monitoring. This study proposes a model that combines a long short-term memory (LSTM) neural network with a localized attention mechanism (LAM), which weights the features using attention weights and connects them to a fully connected output layer. The model is constructed using TEC data from 16 Global Navigation Satellite System (GNSS) observation stations provided by the Crustal Movement Observation Network of China (CMONOC), along with six parameters: Bz, Kp, Dst, F10.7 indices, and hour of the day. The LAM-LSTM model is compared with the LSTM model and the BP model. Experimental results show that the LAM-LSTM model achieves a root mean square error (RMSE) of 1.35 TECU on average for the test dataset, while the LSTM model has an RMSE of 1.54 TECU, and the BP model has an RMSE of 1.74 TECU. The proposed model exhibits good stability in different geomagnetic conditions and different months.

Monitoring and predicting ionospheric space weather is important for global navigation satellite system (GNSS) navigation, positioning, and communication. Ionospheric total electron content (TEC) is a vital indicator to measure ionospheric space weather. This study utilizes a multichannel convolutional long short-term memory (ConvLSTM) with attention mechanism to predict ionospheric TEC maps considering the relevance of physical observations for TEC variations. The MConvLSTM-Attention method is trained and tested on regional ionospheric maps (RIMs) for three years (2015 to 2017) by using GNSS observations from the Crustal Movement Observation Network of China (CMONOC). The experimental results show that the root mean square error (RMSE) values of MConvLSTM-Attention model during quiet, moderate, and geomagnetic storm periods are 2.58, 2.60, and 3.21 TECU, respectively. Also, the MConvLSTM-Attention model performs better than MConvLSTM, ConvLSTM, and international reference ionosphere (IRI) 2016 models in predicting regional ionospheric maps (RIMs). In addition, the MConvLSTM-Attention prediction model shows good generalization performance and relatively good stability and high precision during both geomagnetic quiet and storm time.

As a supplement to time-series interferometric synthetic aperture radar (TS-InSAR), time-series multiple-aperture InSAR (TS-MAI) can measure the spatiotemporal changes in SAR along-track surface deformation. TS-MAI is often applied with low-frequency SAR data (e.g., L-band data) due to its ability to retain high interferometric coherence. However, the low-frequency SAR signal is vulnerable to ionospheric delays, which can significantly degrade the measurement accuracy of TS-MAI. This letter presents an approach to correct the ionospheric errors in TS-MAI. A polynomial cubic model is employed to constrain the ground deformation, which is then incorporated into the observation model for effectively separating the deformation signal and the ionospheric delays. The proposed method is tested using the L-band ALOS-1 PALSAR-1 datasets covering the Tocopilla area in Chile between November 2007 and March 2011. The correction performance and accuracy of the proposed method are demonstrated by comparing the range split-spectrum interferometry (RSSI)-based method and the local GPS data, respectively. The root mean square error (RMSE) improvement rates between TS-MAI and GPS are 72.17% for the SRGD site and 84.51% for the VLZL site, and their correlation coefficients increase from 0.23 and 0.50 to 0.52 and 0.61 after the correction.

Medium-scale traveling ionospheric disturbances (MSTIDs) to the June 22 and 23, 2015 geomagnetic storm over Japan are investigated by using the Global Navigation Satellite System Earth Observation Network of Japan (GEONET) in this letter. We have detected two groups of MSTIDs on June 22 and 23 and a negative ionospheric disturbance response on June 22 observed by the high-resolution detrended total electron content (TEC) maps from GPS observations. The negative ionospheric response (IR) performs a large-scale ionosphere depletion covering a wide range of areas (~125°E to ~140°E and ~25°N to ~40°N) simultaneously, which starts at 1900 universal time (UT) [04:00 local time (LT)] with a period of ~30 min on June 22. Compared with typical MSTIDs, the nighttime and the daytime MSTIDs in this letter both have a southwestward propagated direction and a propagated velocity of ~87.8 and ~157.8 m/s, respectively. The physical sources of the IRs over Japan during this geomagnetic storm are possibly attributed to the prompt penetration electric field (PPEF) and lower atmospheric gravity waves (AGW) referring to the previous studies. We believe that these findings can contribute to the complement of ionospheric research on MSTIDs over Japan.

Computerized ionospheric tomography is a highly ill-posed inverse problem, and regularization tends to stabilize the problem to provide a unique solution. When a regularization method is used, the choice of an optimal parameter is a key issue. In this article, we propose an adaptive regularization method for 3-D ionospheric tomography based on the U-curve. The proposed approach uses a U-curve method to determine the optimal regularization parameter from Global Navigation Satellite Systems (GNSS) observation data. Comparative case studies are investigated based on GNSS simulated observations and real measurements. The simulation results indicate that the proposed method is superior to the adaptive regularization method based on the L-curve. In addition, we further validate the tomographic results with actual ionosonde station data. The results demonstrate the reliability and superiority of the proposed method compared to traditional methods.

In this letter, we investigate the ionospheric variations associated with the Yaan earthquake that occurred on April 20, 2013 in China by using the total electron content (TEC) derived from ground-based Global Positioning System observations and a global ionosphere map (GIM). Geomagnetic and solar activities are taken into account. First, we focus on the coseismic ionospheric disturbances of the earthquake. The time period of the variations is about 15 min after the seismic rupture, and the maximum amplitude is about 0.1 TEC units. We then examine the preseismic ionospheric anomalies by the TEC values from the GIM and the electron density (Ne) values reconstructed by computerized ionospheric tomography. Temporal variations show that the TEC and Ne values simultaneously increased on April 5-8, 2013, which are 12-15 days before. This increase is possibly related to the earthquake. Spatial analysis shows that anomalies tend to appear around the epicenter and their conjugate points.

Ionosphere tomography is a typical ill-posed problem, and using the ionosphere priori information as the constraints to improve the state of normal equation is an effective approach to solve this problem. In this paper, we impose priori constraints by increasing the virtual observations in $n$-dimensional space. Then, after the inversion region to be gridded, we can form a stable structure between the grids with loose constraints, which greatly improve the state of normal equation. Based on that, to obtain the real-time velocity information of ionosphere electron density, we introduce the grid electron density velocity parameters, which can be estimated with electron density parameters simultaneously. The authors use the new algorithm and the global navigation satellite system data in Europe to inverse the 12 ionosphere electron density and velocity images on August 15, 2003, which reflects the ionosphere changes of electron density and velocity in whole day; in addition, we compare the results with the traditional algorithm multiplicative algorithm reconstruction technique's results. Furthermore, we compare the results with the changes time series of plasma frequency observed by ionosphere ionosonde among different layers, and many types of analysis and comparison verify the effectiveness and reliability of the new algorithm. The related research provides a new way for the real-time detection and prediction of ionosphere changes.

The computerized ionospheric tomography usually involves solving an ill-posed inversion problem. The sparsity of Global Positioning System (GPS) stations and the limitation of projection angles lead to insufficient data acquisition, thereby preventing the accurate reconstruction of ionospheric-electron-density distributions. In this paper, we investigate and propose a 3-D iterative reconstruction algorithm based on the minimization of total variation under quiescent and disturbed ionospheric conditions. Numerical experiments on GPS simulation data and real data are discussed. In contrast to the improved algebraic reconstruction technique, the proposed algorithm exhibits significantly reconstruction accuracy.