Shuangshuang Shi

Defining a standardized ionospheric storm scale is challenging given the ionosphere's inherent complexity and susceptibility to multiple driving factors. In this study, a novel ionospheric storm scale (ISS) index is developed based on statistical analysis of total electron content (TEC) data from 257 ground‐based GNSS stations across China between 2008 and 2021. Designed for broad applicability, the index is independent of season, local time, and geographic location. We first derive an ionospheric activity index in percentage by comparing observed TEC values with a quiet‐time reference. To account for seasonal, diurnal, and latitudinal variations in TEC fluctuations, each percentage deviation is normalized using the robust Z‐score method. The ISS is then established by defining thresholds for the normalized data, categorizing activity into seven levels: ISS0, ISS _P 1, ISS _P 2, ISS _P 3, ISS _N 1, ISS _N 2, and ISS _N 3. Results reveal that the new index exhibits no diurnal or seasonal dependence, a key requirement for a homogeneous ionospheric activity time series. Correlation analysis between the ionospheric activity index and the residuals of the ionospheric TEC model we developed previously, along with variations in the index during geomagnetic storms, demonstrates that the ISS can serve as a reliable indicator of the expected performance of ionospheric models and well represents ionospheric activity. Finally, analysis of the relationship between standard point positioning accuracy and the ionospheric activity index under different solar activity levels reveals that higher ionospheric activity corresponds to increased positioning errors during periods of high solar activity.

Large-scale WebGIS visualization of 3D building models is often constrained by large requested payloads, client-side memory pressure, and runtime state-parsing overhead. This study proposes a five-dimensional orthogonal decoupling framework and a 16-bit state-word-driven scheduling method for 3D building models. The Boundary-based Spatial Proxy–Geometric Detail–Component Complexity–Texture Appearance–Semantic Information (B-D-C-T-S) framework organizes model representations into five separately addressable and schedulable dimensions, covering spatial proxies, geometry, components, textures, and semantics. A compact 16-bit structured state word is used to represent runtime states and reduce dependence on repeated text-based state parsing, supporting fixed-offset bitwise decoding, exclusive-OR (XOR)-based differencing, constraint checking, and incremental updating. A centroid-assigned Home Tile strategy is further introduced to reduce redundant semantic payloads for cross-tile objects. The method was evaluated using a single-building BIM model and an urban-scale photogrammetric mesh dataset. Under the tested initial-view setting, staged decoupled loading reduced the first-screen requested payload by 93.1% compared with monolithic loading. State-word-based C-field extraction achieved an approximately 144-fold speedup over JSON deserialization and C-field lookup. The Home Tile strategy reduced the total semantic payload by 44.1% in the semantic-redundancy test. In the 1.12 GB first-screen memory test, state-word-driven D1 tile scheduling loaded only 22.7 MB of physical payload, with stable resident memory of approximately 88.1 MB. These results indicate that the proposed method supports object-level state representation, selective resource activation and scheduling, Home Tile semantic routing, incremental updating, and first-screen memory control within tiled Web3D pipelines.

A high-precision storm-time ionospheric model is crucial for both ionospheric research and precise positioning applications of Global Navigation Satellite Systems (GNSS). However, the determination of an exact functional model remains challenging due to the numerous and complex factors that influence ionospheric variations. Fortunately, the growing availability of GNSS measurements and the rapid developments in computational power offer promising opportunities to address this issue. This study proposes an advanced hybrid model that combines a bi-directional long short-term memory (BiLSTM) network, a convolutional neural network (CNN), and the random forest (RF) algorithm to forecast the ionospheric total electron content (TEC). The model is developed using storm-time data from 257 ground-based GNSS stations in the Crustal Movement Observation Network of China, covering the period from 2008 to 2021. The results indicate that the proposed RF-BiLSTM-CNN model outperforms the BiLSTM, CNN, and PSO-NN models. The RF-BiLSTM-CNN model demonstrates improvements of 15%-46%/14%-48%/37%-72%, 27%-51%/29%-52%/49%-75%, and 33%-55%/33%-56%/52%-72% over the BiLSTM/CNN/PSO-NN model during weak, moderate, and severe geomagnetic storms, respectively. Under weak geomagnetic storm conditions, the root mean square error (RMSE) of the RF-BiLSTM-CNN model within geographic latitude ranges of 40°N to 50°N, 30°N to 40°N, and 20°N to 30°N are between 0.54-0.60, 0.57-0.91, and 1.07-1.70 TECU, respectively. The corresponding RMSE values for moderate (severe) geomagnetic storms are 0.80-0.92, 0.82-1.26, and 1.43-1.95 (1.15-1.30, 1.16-1.70, and 1.71-2.64) TECU, respectively. Furthermore, the RF-BiLSTM-CNN model effectively captures complex TEC variations across all storm intensities, with predictions closely aligning with independent GNSS observations.

Grid-wise Vienna Mapping Functions 1 (VMF1) and Vienna Mapping Functions 3 (VMF3) tropospheric products are widely used to interpolate the a priori zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) over GNSS (Global Navigation Satellite Systems) stations for the mitigation of tropospheric delays contained in GNSS observations. Since these two products provide ZHD and ZWD values only for the ground surface at global grid points, the ZHD and ZWD at the four grid points nearest to a GNSS site used to interpolate for the GNSS site need to be reduced to the same height of the GNSS station before a horizontal interpolation (e.g., bilinear interpolation) is implemented. However, the accuracy of the officially recommended reduction model may not be as good as desired, especially in the case that the height of the GNSS site largely differs from that of the four ground grid points. To address this, a new vertical reduction model for reducing the ZHD and ZWD at the height of grid points to a target height was developed. The sample data for the modelling were the 10-year (2010–2019) ZHD and ZWD profiles over grid points obtained from ERA5 monthly averaged reanalysis data, while the 3-year (2020–2022) radiosonde profiles and the IGS (International GNSS Service) site-wise zenith tropospheric delay (ZTD) products were used to evaluate the new model. Results demonstrated that the accuracy of ZHD, ZWD, and consequently ZTD values interpolated from VMF1/VMF3 products and the new model considerably improved compared to traditional methods, especially at the target sites that have large height differences from its closest VMF grid points. This improvement has significance for those applications that need to use tropospheric delay corrections, e.g. in GNSS positioning and GNSS meteorology for a desired accuracy.

Grid-wise Vienna Mapping Functions 1 (VMF1) and Vienna Mapping Functions 3 (VMF3) tropospheric products have been widely used to interpolate the a priori zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) over the GNSS (Global Navigation Satellite Systems) stations for the mitigation of tropospheric delays inherited in GNSS observations. Since the two products only provide ground surface ZHD and ZWD for global grid points, the ZHD and ZWD of the four grid points nearest to the GNSS site need to be reduced to the same height of the GNSS site before a horizontal interpolation (e.g., bilinear interpolation or inverse-distance weighted interpolation) is implemented. However, the accuracy of the officially recommended simple reduction model may not be as good as desired if the height of a GNSS site largely differs from that of the four ground surface grid points to be used in the interpolation. In this contribution, a new reduction model for each grid point is developed for reducing the grid-wise ZHD and ZWD to the target height to improve the interpolation performance. The sample data for the modelling were the 10-year (2010–2019) ZHD and ZWD profiles over the grid points obtained from ERA5 monthly-mean reanalysis data, while 3-year (2020–2022) ERA5 hourly reanalysis and IGS (International GNSS Service) site-wise ZTD products were used to evaluate the new model. Test results showed that the accuracy of the ZHD, ZWD (as well as the ZTD) interpolated from the VMF1/VMF3 products deduced by the new model was significantly better than the ones deduced by traditional methods, especially for sites with substantial height disparities from adjacent VMF grid points. It is expected that the new model adds good value to related fields such as GNSS positioning and GNSS meteorology for better performance.

The total electron content (TEC) of ionosphere at low latitudes is significantly influenced by solar-geomagnetic activity and seasonal variations. Traditional ionospheric models often struggle to accurately forecast TEC in low latitudes, which limits the improvement of positioning accuracy for single-frequency GNSS (Global Navigation Satellite System) receivers. This study focuses on the Sichuan and Yunnan areas of China that locate on the northern crest of the equatorial ionization anomaly, utilizing data from 48 stations of the Chinese GNSS network. It employs a Convolutional Long Short-Term Memory network with multi-channel characteristics, combined with the Whale Optimization Algorithm, to construct a WOA-CNN-LSTM model for predicting TEC variations. The results demonstrate that the WOA-CNN-LSTM model outperforms Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Recurrent Neural Network (RNN) models in spatial morphology. In 2015 (a year with geomagnetic storms), the root mean square error (RMSE) values for all four seasons are at or below 1.96TECu, with mean absolute error (MAE) values at or below 1.42TECu, and Pearson correlation coefficients at or above 0.98. In 2019 (a calm year), the RMSE values are all below 0.74TECu, MAE values are all below 0.54TECu, and Pearson correlation coefficients remain at or above 0.95. In terms of temporal variation, the RMSE prediction results for the four observation stations are all at or below 2.75TECu for each of the four seasons in 2015, improving to 1.56TECu in 2019. Therefore, this model significantly enhances ionospheric prediction accuracy in low-latitude regions, benefiting navigation positioning, space environment forecasting, and disaster early warning systems.

Precise regional ionospheric total electron content (TEC) models play a crucial role in correcting ionospheric delays for single-frequency receivers and studying variations in the Earth’s space environment. A particle swarm optimization neural network (PSO-NN)-based model for ionospheric TEC over China has been developed using a long-term (2008–2021) ground-based global positioning system (GPS), COSMIC, and Fengyun data under geomagnetic quiet conditions. In this study, a spatial gridding approach is utilized to propose an improved version of the PSO-NN model, named the PSO-NN-GRID. The root-mean-square error (RMSE) and mean absolute error (MAE) of the TECs estimated from the PSO-NN-GRID model on the test data set are 3.614 and 2.257 TECU, respectively, which are 7.5% and 5.5% smaller than those of the PSO-NN model. The improvements of the PSO-NN-GRID model over the PSO-NN model during the equinox, summer, and winter of 2015 are 0.4–22.1%, 0.1–12.8%, and 0.2–26.2%, respectively. Similarly, in 2019, the corresponding improvements are 0.5–13.6%, 0–10.1%, and 0–16.1%, respectively. The performance of the PSO-NN-GRID model is also verified under different solar activity conditions. The results reveal that the RMSEs for the TECs estimated by the PSO-NN-GRID model, with F10.7 values ranging within [0, 80), [80, 100), [100, 130), [130, 160), [160, 190), [190, 220), and [220, +), are, respectively, 1.0%, 2.8%, 4.7%, 5.5%, 10.1%, 9.1%, and 28.4% smaller than those calculated by the PSO-NN model.