This study used Global Navigation Satellite System (GNSS) observations from the China Crustal Movement Observation Network (CMONOC) and the Kunming Continuously Operating Reference Station (KMCORS) network to investigate ionospheric response characteristics over China during the geomagnetic storm of 4-6 November 2023, and to assess their impacts on CORS-based real-time kinematic (RTK) positioning performance in the low-latitude Kunming region. A quantitative assessment was conducted by integrating regional two-dimensional dTEC (%) maps over China, BeiDou Navigation Satellite System (BDS) Geostationary Earth Orbit (GEO) total electron content (TEC), the rate of TEC index (ROTI), and RTK positioning solutions to evaluate ionospheric disturbances, irregularity activity, and associated degradation in positioning performance. Results indicate that, during geomagnetic storms, ionospheric responses over China exhibit pronounced phase-dependent and latitudinal variations. During the second geomagnetic storm on 5-6 November, positive responses were dominant at mid-to-high latitudes, whereas alternating positive and negative responses were observed at low latitudes. During the recovery phase, the Kunming region successively experienced a positive ionospheric storm lasting approximately 10 h, followed by a negative ionospheric storm lasting about 7 h, with relative TEC variations reaching a maximum of approximately 90%. The GEO TEC time series was consistent with the temporal evolution of the two-dimensional dTEC (%), while ROTI increased markedly during the disturbance enhancement period (21:00 UT on 5 November to 07:00 UT on 6 November 2023). During periods of enhanced ionospheric response and irregularities, RTK positioning performance was observed to deteriorate markedly. The fixed-solution rate at medium-to-long baseline stations decreased from nearly 100% to close to 0%, accompanied by an increase in vertical positioning errors to approximately 20 cm, whereas short-baseline stations were only minimally affected. These results indicate that ionospheric disturbances during geomagnetic storms exert a pronounced impact on CORS-based RTK positioning services in the Kunming region, with the magnitude of this impact being closely related to baseline length.

This article utilizes the China crustal movement observation network GNSS observations, Swarm/COSMIC satellite electron density measurements, and TIMED/GUVI thermospheric composition data to systematically analyze the multiscale ionospheric responses over China during two geomagnetic storm events in March 2023 (CME-driven) and April 2023 (CME-CIR compound driven). Key findings include the following. First, the March CME event exhibited a single-phase development pattern, with SYM-H dropping to -170 nT. The traveling ionospheric disturbances primarily propagate northwestward, synchronous with the dawnside gravity wave-dominated O/N-2; front (>0.6) and the westward migration of detrended DTEC positive disturbances. Second, the April CME-CIR compound event displayed a dual main phase and dual recovery phase pattern, corresponding to SYM-H minima at -132 and -231 nT. This event featured northeastward ultrafast disturbances that were tightly synchronized with the eastward expansion of penetration electric field-driven plasma bubbles, alongside equatorial O/N-2; collapse from 0.9 to 0.4 and reconstruction of midlatitude compositional fronts (O/N-2;>0.9). Third, both events exhibited significant TEC enhancements at midlatitudes and abrupt electron density decreases during the later main phase. However, the disturbance propagation mechanisms show fundamental differences: the CME event triggers gradient drift instabilities through interactions with neutral wind fields and gravity waves, while the CME-CIR compound event triggered a sequence of disturbances by electric field penetration and compositional imbalance. This study constructs a 3-D quantitative framework linking electric field-driven changes, compositional variations, and instability development, providing a new quantitative tool to distinguish different drivers (pure CME versus CME-CIR compound) with ionospheric response patterns.

Abstract The ionospheric recovery responses to two G5 geomagnetic storms over China in May and October 2024 were compared using GNSS‐derived total electron content (TEC), detrended TEC, the rate of TEC index (ROTI), magnetometer data, and GUVI/TIMED O/N 2 observations. The results show that the May recovery phase was dominated by widespread and persistent negative TEC anomalies, with low‐latitude depletion reaching approximately 60 TECU. In contrast, the October event exhibited a pronounced latitudinal structure, including a C‐shaped positive enhancement at mid‐latitudes and low‐latitude depletion of approximately 50 TECU. The elevated O/N 2 ratio may have favored the mid‐latitude enhancement. Although substantial low‐latitude TEC depletion occurred on both May 11 and October 11, no pronounced ROTI enhancement was observed in October, suggesting that the depletion was mainly associated with weakened electrodynamic forcing, contraction of the EIA, and reduced electron content in the upper ionosphere. Six groups of large‐scale traveling ionospheric disturbances (LSTIDs) and two types of ionospheric irregularities were identified in May, compared with three LSTID groups and one type of brief, localized irregularity in October. These differences suggest that high‐latitude energy input, low‐latitude electrodynamics, and thermospheric composition jointly controlled the contrasting ionospheric recovery responses.

Accurate forecasting of ionospheric Total Electron Content (TEC) is important for high-precision navigation and positioning. In this study, we develop a hybrid forecasting model by integrating a Temporal Convolutional Network (TCN), a Bidirectional Long Short-Term Memory (BiLSTM) architecture, and a Multi-Head Attention mechanism, hereinafter named TCN-BiLSTM-MHATT. The model uses the 15th-order spherical harmonic functions adopted by the Centre for Orbit Determination (CODE) to express global ionospheric TEC. It predicts the 256 spherical harmonic coefficients using CODE historical coefficients as inputs. We conducted 24-hour rolling forecast experiments with a 1-h interval for periods representing both low solar activity (0 < F10.7 < 80) and high solar activity (100 < F10.7 < 350). The predicted coefficients were then used to reconstruct global ionospheric maps (GIMs). Model performance was evaluated against the C1PG product, standalone LSTM and BiLSTM models, and a LSTM-based hybrid model built with the same framework (named TCN-LSTM-Multi-Head Attention), using CODG as the benchmark. The results indicate that, compared to the standalone BiLSTM model, the TCN-BiLSTM-Multi-Head Attention model reduces the overall RMSE by 10.3% during the low solar activity period and by 9.3% during the high solar activity period. Overall, the proposed hybrid model improves the average forecasting accuracy by 17.01%, 9.82%, and 62.72% compared with the standalone LSTM, standalone BiLSTM, and C1PG models, respectively. To further examine the global and local spatial performance, GIMs for two representative quiet and geomagnetic storm days were analyzed, and TEC variations along longitude were investigated for six latitude sectors ranging from 87.5°S to 87.5°N. The results demonstrate that the proposed hybrid model provides a robust and accurate solution for short-term global TEC forecasting under both solar activity conditions.

The Total Electron Content (TEC) is a key parameter for characterizing the ionosphere, and accurately forecasting TEC during geomagnetic storms is essential for modern communication and navigation systems. In this study, a PredRNN++ based ionospheric TEC prediction model is proposed to estimate TEC variations under both quiet and storm-period conditions. The model utilizes 1-hour Global Ionospheric Maps (GIM) as the primary dataset, and 22 geomagnetic storm events from Solar Cycle 24 (2011-2018) were selected based on the Dst index. The Dst and F10.7 indices were incorporated as additional input channels to enhance the model's generalization capability. A detailed analysis of the PredRNN++ model's forecasting performance was conducted for different geomagnetic storm phases-including quiet, initial, main, and recovery phases-within the test events. The results show that PredRNN++ achieves excellent forecasting accuracy across all phases, outperforming previous models. Furthermore, its performance was compared with five advanced baseline models: GRU, LSTM, ConvLSTM, PredRNN, and COPG (the one-day prediction product released by CODE). The evaluation across different latitudes and storm phases demonstrates that PredRNN++ consistently outperforms all other models. During both quiet and storm periods, the PredRNN++ model exhibited the best overall performance. Specifically, for the 2017 geomagnetic storm event, the RMSE values were 0.82 TECU (initial phase), 1.31 TECU (main phase), and 0.89 TECU (recovery phase), showing a clear improvement in accuracy compared to other models. For the 2018 geomagnetic storm event, the RMSE values were 0.50 TECU (initial phase), 0.69 TECU (main phase), and 0.76 TECU (recovery phase), indicating significantly higher prediction precision. Additionally, since the strongest geomagnetic storm intervals exert a substantial influence on forecasting performance, the model's behavior was further analyzed during the most intense periods. The results confirm that PredRNN++ achieves the best prediction accuracy at the storm's maximum intensity, demonstrating its robustness and effectiveness in modeling complex ionospheric dynamics during severe geomagnetic disturbances. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

On January 15,2022,the violent eruption of the Tonga Volcano triggered significant ionospheric disturbances,resulting in the disruption of GNSS signal propagation and impairing the accuracy of Precise Point Positioning(PPP).This paper provides a systematic analysis of the characteristics of the ionospheric disturbances and their impact on positioning accuracy by utilizing data from the BeiDou Navigation Satellite System(BDS)and multi-source observations.Utilizing the characteristics of fixed ionospheric pierce point(IPP)from BeiDou GEO satellites,key parameters such as disturbance propagation speed and Total Electron Content(TEC)variations were extracted.Furthermore,an improved Disturbance Index(DI)was proposed to quantify the intensity of ionospheric disturbances.The results reveal that the propagation speed of Traveling Ionospheric Disturbances(TIDs)is approximately 290 m-s-1,similar to the speed of Lamb waves,suggesting a strong coupling between the disturbances and atmospheric waves.Additionally,it was found that the volcanic eruption resulted in a significant decrease in TEC,the appearance of Equatorial Plasma Bubbles(EPBs),localized enhancements,and scintillation phenomena.Particularly noteworthy is the local enhancement of the ionosphere observed above the TOW2 station.Combining data from Swarm,COSMIC-2,and ICON satellites,further verification of the variations in electron density,ion temperature,and O/N2 ratio was conducted.PPP accuracy analysis demonstrated a marked increase in positioning errors during the disturbance and scintillation periods,particularly in northern Australia and low-latitude regions.This event underscores the significant impact of ionospheric disturbances on GNSS systems and emphasizes the importance of ionospheric modeling in enhancing navigation and positioning accuracy.

Satellite Clock Bias (SCB) is a major source of error in Precise Point Positioning (PPP). The real-time service products from the International GNSS Service (IGS) are susceptible to network interruptions. Such disruptions can compromise product availability and, consequently, degrade positioning accuracy. We introduce the CNN-LSTM-Attention model to address this challenge. The model enhances a Long Short-Term Memory (LSTM) network by integrating Convolutional Neural Networks (CNNs) and an Attention mechanism. The proposed model can efficiently extract data features and balance the weight allocation in the Attention mechanism, thereby improving both the accuracy and stability of predictions. Across various forecasting horizons (1, 2, 4, and 6 h), the CNN-LSTM-Attention model demonstrates prediction accuracy improvements of (76.95%, 66.84%, 65.92%, 84.33%, and 43.87%), (72.59%, 65.61%, 74.60%, 82.98%, and 51.13%), (70.45%, 68.52%, 81.63%, 88.44%, and 60.49%), and (70.26%, 70.51%, 84.28%, 93.66%, and 66.76%), respectively, across the five benchmark models: Linear Polynomial (LP), Quadratic Polynomial (QP), Autoregressive Integrated Moving Average (ARIMA), Backpropagation Neural Network (BP), and LSTM models. Furthermore, in dynamic PPP experiments utilizing IGS tracking stations, the model predictions achieve positioning accuracy comparable to that of post-processed products. This proves that the proposed model demonstrates superior accuracy and stability for predicting SCB, while also satisfying the demands of positioning applications.

On 10 May 2024, a severe G5 geomagnetic storm-the most intense of solar cycle 25-significantly disturbed the global ionosphere. This study presents a comprehensive analysis using multi-instrument observations, including ground-based measurements from BDS-GEO total electron content (TEC), digital ionosondes, and magnetometers; model outputs from global ionospheric maps, HWM14, and PPEFM; and satellite data from DMSP, FY-3E, TIMED, and Swarm. Focusing on the Asia-Pacific sector along 120 degrees E, the BDS-GEO satellites provided continuous, fixed-point monitoring of ionospheric TEC disturbances at this longitude. Hemispheric asymmetry was evident: the main phase showed weak ionospheric responses due to the local midnight conditions and plasma uplift driven by nocturnal eastward overshielding electric fields. During the recovery phase, pronounced latitudinal differentiation emerged. A persistent "delayed negative response" occurred in the Northern Hemisphere (NH) at mid-to low-latitudes, intensifying the following day with TEC depletions exceeding 20 TECU at multiple stations. In contrast, the Southern Hemisphere (SH) equatorial and low-latitudes displayed a "negative-then-positive" disturbance pattern, accompanied by a weakened and nocturnally intensified equatorial ionization anomaly twin-crest structure. Mechanistic analysis indicates that the observed O/N2 depletion from TIMED/GUVI, combined with strong equatorward disturbance winds from HWM14, amplified the NH negative storms through coupled thermospheric composition changes and summer circulation. Meanwhile, SH low-latitude disturbances were modulated by the competing effects of prompt penetration electric field and disturbance dynamo electric fields, with the background winter circulation suppressing horizontal N2 transport while vertical motion enhanced oxygen supply, maintaining higher O/N2 ratios. This study indicates the dominant physical mechanisms across latitudes during an extreme geomagnetic storm.

Typhoons, as intense atmospheric disturbances, are capable of significantly influencing ionospheric conditions, primarily through the excitation of gravity waves, which transfer energy into the ionosphere. This study investigates the characteristics of ionospheric disturbances during 1-8 September 2024, when Typhoon YAGI affected the study region. Data from GNSS ground stations, COSMIC-2 LEO satellites, and TIMED/GUVI thermospheric composition data, in conjunction with the horizontal wind model (HWM14), were utilized to examine the coupling mechanisms between typhoons and the ionosphere. The results indicate that Typhoon YAGI induced pronounced ionospheric disturbances along its trajectory, with propagation velocities ranging from 127 m/s to 339 m/s, consistent with those of medium-scale traveling ionospheric disturbances. Notably, within the fixed longitude range of 120 degrees +/- 5 degrees E, the Beidou GEO TEC results based on the inversion of MEGX stations in the range of 0 degrees N-25 degrees N, as well as the COSMIC-2 electron density profiles, indicate that there is a clear north-south difference in the ionospheric response near the latitude of 17 degrees N, with the northern region showing a positive response in terms of increased electron density, while the negative response in the south. In addition, the equatorial electric field and equatorial electrojet analyses show that the electric and magnetic field activities remained relatively calm during the typhoon. However, on 4 September, the southward turning of the IMF Bz triggered a westward penetrating electric field, which significantly reduced the north-south asymmetry of the ionospheric response. Meanwhile, the background wind speed was estimated by the HWM14 model to be low, suggesting a relatively stable atmospheric background. The above results further reveal the coupling between atmospheric gravity waves and the ionosphere, as well as the modulation effect of the topography and background wind field on the disturbance propagation.

To address the issues of low accuracy and limited spatial resolution in traditional single data source precipitable water vapor (PWV) predictions, based on the strong spatiotemporal characteristics of PWV, we constructed a high-accuracy, high-spatial-resolution continuous real-time PWV prediction model based on multi-source PWV data fusion. The PWV data include Global Navigation Satellite System (GNSS) PWV and the fifth generation of the European Centre for Medium-Range Weather Forecasts (ERA5) PWV. Feature engineering is incorporated into the traditional Transformer and long short-term memory (LSTM) models to improve their feature extraction abilities for time series data. The real-time PWV monitoring is achieved by the short-term sliding window technique in the prediction models. Results show that the root mean square error (RMSE) of the Transformer predictions based on fused PWV decreases from 1.596 mm to 1.253 mm, a reduction of 21.49 %, while the correlation coefficient (R) increased from 0.967 to 0.979, an improvement of 1.24 %. For the LSTM predictions, RMSE decreases from 1.601 mm to 1.3 mm, a reduction of 18.83 %, and R increased from 0.967 to 0.979, an improvement of 1.24 %. The fused PWV data outperforms the sole ERA5 PWV in real-time monitoring, and the Transformer model performs better than the LSTM model in short-term PWV predictions.

Precipitable water vapor (PWV) as an important parameter of atmospheric water vapor can be obtained through global navigation satellite system (GNSS), which offers high-precision observations in all-weather conditions. It is difficult for the current meteorological observing systems to measure PWV under extreme weather conditions. GNSS-derived PWV has high temporal and spatial resolutions, being a reliable and stable data source for meteorology. In this study, we establish an empirical model for the atmospheric weighted mean temperature (Tm) in Yunnan, China based on radiosonde data collected from 2020 to 2022. The model is verified by assessing its accuracy using measurements of four radiosondes in 2023. The results show that the empirical model is more accurate compared to other T m widely used models such as GPT2w model, Li model, and Bevis model. The root mean square (RMS) of obtained from the empirical T m models are 3.5 K, 2.3 K, 2.1 K and 2.1 K with average bias of 0.10 K, 0.22 K, 0.04 K, and 0.32 K, respectively. We use GNSS to obtain PWV and validate the GNSS-derived PWV using data from the radiosonde station in Kunming, Yunnan. The GNSS PWV is consistent with the PWV measured by the radiosondes. The RMS of the GNSS-PWV of the years 2020, 2021, and 2022 are 3 mm, 2.2 mm, and 2.9 mm, respectively, and the GNSS-PWV is confirmed to have a higher accuracy compared to others. Additionally, this study analyzes the spatiotemporal variations of GNSS PWV over Yunnan in 2021. The result shows that GNSS-derived PWV varies with geographic location and season and is significantly related to precipitation. Through the analysis of PWV during drought periods in 2021 and comparison with the same period in 2020 and 2022, we find that PWV is significantly lower in 2021 than in the same period of other years. Understanding the spatiotemporal distribution of PWV is crucial for meteorological monitoring and weather forecasting. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The ionospheric Total Electron Content (TEC) is a crucial parameter for studying ionospheric variations and space weather. Accurately predicting the spatiotemporal changes in TEC and constructing precise ionospheric forecast models are of great significance. This study proposes a BiLSTM-GRU-Attention combined model, which integrates Bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) networks, and finally applies the attention mechanism to assign different weights to each forecasted value, obtaining the final prediction. Data from 24 Global Navigation Satellite System (GNSS) observation stations across China for the period 2012 to 2019, provided by the Crustal Movement Observation Network of China (CMONOC), are used in this study. The years 2015 (high solar activity) and 2019 (low solar activity) are selected as the test sets. The results of this model are compared in detail with Moth-flame optimized Least Squares Support Vector Machine (MFO_LSSVM), original GRU, and BiLSTM-GRU models. During low solar activity year, the BiLSTM-GRU-Attention model demonstrated stability across high, mid, and low latitudes without significant performance variations among stations at different latitudes. In annual forecasts, the BiLSTM-GRU-Attention model was less affected by geomagnetic storm activity, achieving average improvements of 9.1%, 8.7%, and 2.6%, respectively, compared to MFO_LSSVM, original GRU, and BiLSTM-GRU models. In high solar activity year, the BiLSTM-GRU-Attention model exhibited better forecasting performance across all latitudes, particularly in high-latitude regions. For annual forecasts, the BiLSTM-GRU-Attention model remained stable. During different geomagnetic activity periods, especially during quiet and moderate conditions, it achieved higher accuracy compared to other models. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The ionospheric total electron content (TEC) is a crucial parameter for studying ionospheric variability and space weather. Short-term forecasting of the ionosphere is also vital for detecting near-Earth space environment changes. This study proposes a random forest (RF) feature selection method combined with the Prophet model. The best features are selected by random forest training, and the Prophet model is used to forecast ionospheric TEC data for the short term. This paper presents the construction of the RF-Prophet model using TEC data from 16 GNSS stations at CMONOC and five parameters selected through training, namely Lyman alpha, Auroral Electrojet Index (AU index), Polar Cap Index (PC-index), Auroral Lower Index (AL index), and Solar Radio Flux at 10.7 cm (F10.7). The model's performance is evaluated by comparing it with a single Prophet model, using 30 days of historical TEC data as the training set and selecting a one-day sliding forecast experiment for the 2015 high solar activity and 2018 low solar activity years. The experimental results indicate that the RF-Prophet model has a lower one-year average root mean square error (RMSE) of all stations in the 2015 and 2018 test sets compared to the Prophet model. Specifically, in the 2015 test set, the RMSE of the RF-Prophet model improved by 9.40 % compared with the Prophet model; in the 2018 test set, the RMSE of the RF-Prophet model improved by 7.19 % compared with the Prophet model. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

The ionospheric total electron content (TEC) in the ionosphere is a crucial parameter for studying ionospheric variations and space weather. Short-term prediction of the ionosphere is of significant importance for near-Earth space environment monitoring. This study proposes a hybrid model combining Dung Beetle Optimization (DBO) algorithm and Long Short-Term Memory (LSTM) neural network. By optimizing the number of neurons in the LSTM network, the dropout rate, the number of neurons in the fully connected layer, and the batch size, the prediction accuracy of the model can be improved to a certain extent. This paper utilizes TEC data from 24 GNSS observation stations of Crustal Movement Observation Network of China (CMONOC) and five parameters selected through Random Forest training, including f10.7, Lyman_alpha, SW Plasma Speed, Dst, and R Sun Spot indices to train the model. By forecasting ionospheric TEC during geomagnetic storms and comparing the model's prediction results with those of LSTM and RNN models, it is found that the DBO-LSTM model outperforms the LSTM and RNN models on the test set. During severe geomagnetic storms, the RMSEs of the DBO-LSTM, LSTM, and RNN models at the low-latitude station GDZH are 5.34 TECU, 5.91 TECU, and 6.38 TECU, respectively. The proposed model demonstrates good predictive performance during geomagnetic storms. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

We investigate ionospheric disturbances over China during the May 2017 geomagnetic storm using an integrated data set, including total electron content (TEC) measurements from BeiDou Navigation Satellite System's (BDS) Geostationary Earth Orbit (GEO) observations, ionosonde data, Swarm satellites, and global navigation satellite system (GNSS) radio occultation (RO) data. TEC anomalies were identified using the Prophet forecasting model, and results were compared with three sliding time window methods, showing consistent outcomes. The significant TEC increase during the storm's main phase was driven by prompt penetration electric fields (PPEF) linked to interplanetary magnetic field (IMF) Bz fluctuations. During the recovery phase, TEC increased on May 29 night, associated with southward IMF Bz turning and westward PPEF. Notably, the TEC negative storm observed at the KUN1 station on the morning of May 29 was likely caused by F‐layer uplift driven by the eastward overshielding electric field (OPEF) during the recovery phase, which induced Rayleigh‐Taylor instability and resulted in the formation of plasma bubbles. Additionally, the electron density (Ne) measured by COSMIC and Swarm satellites, along with ionospheric F2 layer parameters critical frequency (foF2) and peak height (hmF2) showed significant increases during the storm. A negative ionospheric response was observed over China on May 30 from 00:00 to 12:00 UT, likely caused by thermosphere composition changes. This study highlights the efficiency of BDS‐GEO satellites in monitoring ionospheric TEC variations, capturing spatiotemporal characteristics of disturbances, and validating the Prophet model for detecting anomalous TEC fluctuations during geomagnetic storms.

Anomalous ionospheric disturbances notably impact the precision and reliability of global navigation satellite system positioning. With a tendency to manifest in low-latitude regions, they are challenging to anticipate with certainty. We used data from about 100 continuous operating reference stations (CORS) to examine ionospheric disturbances in the low-latitude region in Yunnan, China. We calculated total electron content (TEC) and detrended TEC, determined the intensity and range of disturbances, and evaluated the impact of anomalous disturbances on positioning precision using the precise point positioning with ambiguity resolution (PPP-AR) method for a single BeiDou navigation satellite system (BDS) at low-latitudes. The analysis focused on the data collected during UT 04:00–05:00 and UT 19:00–20:00 on DOY 116 of 2023. The results demonstrated that anomalous ionospheric disturbances at low latitudes markedly prolong the convergence time of a single BDS PPP-AR and reduce the precision of positioning. These effects are especially pronounced in phases and regions with frequent and strong ionospheric activities. To overcome these challenges, this study proposes an improved approach by increasing the satellite elevation angle mask above 25° to mitigate the influence of low-angle signals due to ionospheric perturbations. In this way, the stability and accuracy of single BDS PPP-AR positioning in low-latitude regions are significantly improved.

Ionospheric data assimilation aims to address the uneven spatiotemporal distribution of observational data and errors in numerical models. This paper proposes an ionospheric data assimilation model using the hierarchical Bayesian network (HBN) algorithm. We use the International Reference Ionosphere (IRI) 2016 as a background model. The HBN method assimilates global navigation satellite system (GNSS) observational data from approximately 260 stations within the Crustal Movement Observation Network of China (CMONOC). For this analysis, we use the total electron content (TEC) data from the Center for Orbit Determination in Europe (CODE) and BeiDou Navigation Satellite System (BDS) geostationary earth orbit (GEO) experiments. We evaluate the HBN assimilation effect through single-frequency precise point positioning (PPP). The results demonstrate that the HBN algorithm closely aligns with the BDS GEO TEC, regardless of geomagnetic conditions. Statistical results show that, with BDS GEO TEC data as the ground truth reference, the HBN model improves the correlation coefficient by approximately 14% and reduces the root mean square error (RMSE) by around 33% compared to the IRI model. The assimilation effect is significantly superior to that of the Kalman filter. Additionally, the HBN-based PPP method demonstrates slightly improved GNSS positioning accuracy compared to CODE-based PPP, with a reduction in RMSE observed under both geomagnetically disturbed and quiet conditions. Thus, the HBN method is effective for ionospheric data assimilation.

Real-time ionospheric products can accelerate the convergence of real-time precise point positioning (PPP) to improve the real-time positioning services of global navigation satellite systems (GNSSs), as well as to achieve continuous monitoring of the ionosphere. This study applied an extended Kalman filter (EKF) to total electron content (TEC) modeling, proposing a regional real-time EKF-based ionospheric model (REIM) with a spatial resolution of 1° × 1° and a temporal resolution of 1 h. We examined the performance of REIM through a 7-day period during geomagnetic storms. The post-processing model from the China Earthquake Administration (IOSR), CODG, IGSG, and the BDS geostationary orbit satellite (GEO) observations were utilized as reference. The consistency analysis showed that the mean deviation between REIM and IOSR was 0.97 TECU, with correlation coefficients of 0.936 and 0.938 relative to IOSR and IGSG, respectively. The VTEC mean deviation between REIM and BDS GEO observations was 4.15 TECU, which is lower than those of CODG (4.68 TECU), IGSG (5.67 TECU), and IOSR (6.27 TECU). In the real-time single-frequency PPP (RT-SF-PPP) experiments, REIM-augmented positioning converges within approximately 80 epochs, and IGSG requires 140 epochs. The REIM-augmented east-direction positioning error was 0.086 m, smaller than that of IGSG (0.095 m) and the Klobuchar model (0.098 m). REIM demonstrated high consistencies with post-processing models and showed a higher accuracy at IPPs of BDS GEO satellites. Moreover, the correction results of the REIM model are comparable to post-processing models in RT-SF-PPP while achieving faster convergence.

To address the issues of high noise in ionospheric total electron content (TEC) time series data, fluctuations in prediction accuracy with changing space environments, and the unclear mechanism and process of selecting space physical parameters for model training, this article uses the random forest and deep denoising autoencoder models to construct a method for assessing the importance of space physical parameters and enhancing ionospheric TEC data. Combining this with an improved transformer prediction model, a data-driven method for TEC forecasting that takes into account the influence of the space environment is proposed. This method is applied to the global ionosphere map (GIM) data provided by the Center for Orbit Determination in Europe (CODE) for the solar minimum year of 2018 and the solar maximum year of 2015, and compared with traditional transformer models and CODE's 1-day forecast product C1PG. The experimental results show that while the proposed method did not show a significant improvement in accuracy compared to the models that did not consider the space environment in the solar minimum year of 2018, it achieved a noticeable accuracy improvement in the solar maximum year of 2015. The accuracy improvements in terms of the annual root-mean-square error (RMSE), annual mean absolute error (MAE), and annual correlation coefficient are 4.2%, 4.17%, and 0.132% respectively, indicating a significant enhancement.