In recent years, precise point positioning (PPP) technology has undergone rapid development. In particular, since the International GNSS Service (IGS) began providing multi-system real-time products, real-time PPP has been widely applied in areas such as displacement monitoring, atmospheric sounding, and geodetic surveying. Although real-time products generally demonstrate stable performance and meet application requirements under most conditions, anomalies in the real-time product are inevitable. These anomalies include low product availability, degraded accuracy, and even abnormal results. Currently, several IGS analysis centers provide real-time products; however, none of them include corresponding integrity information. In this study, we first developed a real-time PPP augmentation service, which utilizes globally distributed tracking stations to determine satellite orbits, clocks, and uncalibrated phase delays (UPDs). Furthermore, this study provides a detailed analysis of integrity information, such as quality indicators (QI) for each system based on characterization of phase residuals. Finally, validation of the real-time PPP augmentation system using 24 globally distributed stations demonstrates average positioning accuracies of 0.012 m, 0.014 m, and 0.031 m in the North, East, and Height components, respectively, with an average convergence time of 129 s and a wrong-fix rate of 2.225%. The results indicate that under nominal service conditions, integrity information has minimal impact on positioning accuracy, though it slightly reduces the probability of wrong-fix rate and the time-to-first-fix (TTFF). However, during satellite orbital maneuvers or periods of degraded accuracy and anomalies, QI significantly enhances both positioning precision and solution reliability.

ABSTRACT The Equatorial Ionospheric Anomaly (EIA) occurs within the ionosphere on both sides of the Earth's magnetic equator, typically at magnetic latitudes of approximately ± 10° to ± 20°. In this region, the spatiotemporal evolution of ionospheric total electron content (TEC) is highly complex, leading to anomalous enhancements in electron density. These ionospheric disturbances become particularly pronounced during geomagnetic storms, posing significant challenges for high‐precision prediction. To address this prediction challenge, we introduce a deep learning model termed an Encoder‐Decoder with Self‐Attention Convolutional Gated Recurrent Unit (ED‐SA‐ConvGRU), where multiple physical indices were incorporated, including solar activity indices (F10.7, Solar Radio Flux at 10.7 cm; SSN, Sunspot Number) and geomagnetic indices (Kp, K‐index; Dst, Disturbance Storm Time Index), with three distinct input combinations constructed. This study utilised GNSS data from 70 stations of the Australian Regional GNSS Network (ARGN) from 2023 to 2025. Test results indicate that, compared to the GRU, ConvGRU, and ED‐ConvGRU models, the proposed ED‐SA‐ConvGRU model reduced the Root Mean Square Error (RMSE) by 16.8%, 2.4%, and 6.8%, respectively. Moreover, the prediction accuracy of the ED‐SA‐ConvGRU model was further enhanced through the incorporation of physical indices. Specifically, Input Combination III (historical TEC + geomagnetic indices Kp and Dst + solar activity indices F10.7 and SSN) yielded the lowest RMSE. These findings indicate that the ED‐SA‐ConvGRU model has competitive performance in ionospheric TEC prediction over the low‐to mid‐latitude EIA region.

Ray-tracing using numerical weather models (NWMs) represents one of the most precise methods for deriving tropospheric delays in high-precision space geodetic applications. However, considerable computational demands, particularly those associated with input/output operations, interpolation procedures, and data transmission, constrain contemporary global tropospheric delay grid products to coarse spatial resolutions, thereby introducing centimeter-to-decimeter-level biases in large height difference areas. This study introduces TropDS, a novel downscaling framework for tropospheric delays that integrates physical residual modeling with a U-Net artificial intelligence (AI) refinement. By independently characterizing the spatiotemporal deterministic and stochastic components, TropDS produces high-precision, high-resolution products from low-resolution global tropospheric delay fields. The model was trained and validated on the TUW VMF3_OP global grid spanning 2020–2024 and subsequently tested on 2025 data. Evaluation results for the testing year demonstrate that TropDS enhances global zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) accuracies to 90.97 https://github.com/Sardingfish/TropDS .

The traditional global navigation satellite system (GNSS) satellite inter-frequency clock bias (IFCB) estimation methods rely on sequential calculations per epoch and then model fitting, which introduces redundant steps, increases data storage and transmission costs, and reduces the timeliness of real-time services (RTS). To address these limitations, this paper proposes a direct parameterization estimation method for GNSS satellite IFCB, which estimates model coefficients directly from the combined carrier phase and pseudorange observations without first generating the intermediate per-epoch IFCB sequence. This method simplifies the processing flow, reduces computational complexity, and generates a compact parameterized IFCB product with the minimum storage requirements. Validation experiments were conducted using 30 d multi-system GNSS data from 85 international GNSS service (IGS) stations and 3 d data from 20 independent IGS stations for IFCB model building and precise point positioning (PPP) evaluation. The results show that the proposed method has comparable IFCB estimation accuracy to the traditional cycle-based and fitting methods, with errors at the millimeter level. The IFCB model correction significantly improves the convergence and positioning accuracy of PPP: compared to the uncorrected solutions, the three-dimensional root mean square error of GPS static PPP is decreased by approximately 33%, and that of BDS-2 is decreased by approximately 29%. For RTS, direct coefficient estimation reduces processing delay and enables efficient IFCB extrapolation and broadcasting. Real-time PPP tests confirmed that this method is comparable in accuracy to traditional methods while improving timeliness. This method is applicable to GPS, BDS-2, BDS-3, and Galileo systems, providing an efficient and universal solution for multi-frequency GNSS high-precision positioning in post-processing and real-time scenarios.

Millimeter-level transient displacements caused by extreme natural disasters are crucial for studying their dynamic processes. However, at present, such signals are rarely captured in Chinese mainland by GNSS. In October 2014, the passage of Typhoon Vongfong generated a significant storm surge, causing sea levels to rise rapidly within hours. Using 3-h displacements from GNSS stations of the Crustal Movement Observation Network of China (CMONOC) along the eastern coast of China, we evaluated non-tidal ocean loading (NTOL) effects during October 2014 by comparing the GNSS observations with model predictions. According to the NTOL predictions, vertical deformation in coastal areas in eastern China reached 2 cm, but subsidence could only be marginally detected by GNSS. Only at the Zhejiang Zhoushan station (ZJZS), the root mean square (RMS) in the vertical was reduced by 1.64 mm after removing the NTOL predictions. In the future, to capture transient signals, we will focus on integrating multi-GNSS to improve the accuracy of high-rate GNSS positioning.

Over the past few decades, the precise point positioning (PPP) technique has primarily been employed in post-processing applications due to its flexibility and high precision. However, two critical challenges continue to limit its broader application in dynamic and time-sensitive applications. First, there remains a lack of stable and publicly available multi-Global Navigation Satellite System (GNSS), multifrequency real-time products, which are essential for enabling rapid integer ambiguity resolution (IAR). Second, research on large-scale, high-precision ionospheric modeling remains underdeveloped, causing the rapid convergence of real-time PPP to rely heavily on dense regional reference networks. To overcome these limitations, this study develops a PPP augmentation system that provides multi-GNSS and multifrequency corrections to support rapid IAR, which are subsequently uploaded to the IGS caster (products.igs-ip.net). Furthermore, motivated by the demand for accurate ionospheric constraints to enhance convergence across regional and global scales, a modified adjusted spherical harmonic function (ASHF) model is proposed to generate precise ionospheric corrections under sparse network conditions. Real-time GNSS observations from multiple monitoring stations are simultaneously processed to assess the performance of the proposed methods. Statistical analysis indicates that the average time-to-first-fix (TTFF) of PPP-IAR is approximately 50.2 s, with an average positioning accuracy of 0.005/0.005/0.022 m in the North/East/Height components. Moreover, the proposed ionospheric model, based on a network with an average baseline of 244.6 km, achieves roughly a 90% improvement in model accuracy compared to the conventional ASHF method. Finally, validation through PPP-real-time kinematic (RTK) experiments demonstrates a 36.9% reduction in average TTFF-from 62.2 s (PPP-IAR) to 39.6 s (PPP-RTK)-along with a modest improvement in positioning precision, thereby confirming the effectiveness of the proposed approach in addressing the key limitations of real-time PPP.

Pangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predicting temperature, wind speed, wind direction, and barometric pressure using data from over 2000 weather stations in China. Pangu’s performance was compared with ECMWF-HRES and GFS to assess its effectiveness relative to traditional high-precision NWP models under real meteorological conditions. Furthermore, the more recent FuXi and FengWu models were included in the analysis to further validate Pangu’s forecasting skill. The study examined Pangu’s forecast performance from spatial perspectives, evaluated the dispersion of forecast deviations, and analyzed its performance at different lead times and with various initial fields. The iteration precision of Pangu’s four forecast models with lead times of 1 h, 3 h, 6 h, and 24 h was also assessed. Finally, a case study on typhoon track forecasting was conducted to evaluate Pangu’s performance in predicting typhoon paths. The results indicate that Pangu surpasses traditional NWP systems in temperature forecasting, while its performance in predicting wind direction, wind speed and pressure is comparable to them. Additionally, the forecast skill of Pangu diminishes as the lead time extends, but it tends to surpass traditional NWP systems with longer lead times. Moreover, FuXi and FengWu demonstrate even higher accuracy compared to Pangu. Pangu’s performance is also dependent on initial fields, and the temperature forecasting of Pangu is more sensitive to the initial field compared with other meteorological parameters. Furthermore, the iteration precision of Pangu’s 1 h forecast model is significantly lower than that of the other models, but this discrepancy in precision may not be prominently reflected in Pangu’s actual forecasting process due to the greedy algorithm employed. In the case study on typhoon forecasting, Pangu, along with FuXi and FengWu, demonstrates comparable performance in predicting Bebinca’s track compared to ECMWF and outperforms GFS in its track predictions. This study demonstrated Pangu’s applicability in short- to medium-term forecasting of meteorological parameters, showcasing the significant potential of AI-based numerical weather models in enhancing forecast performance.

Although the traditional Carrier-to-Code Leveling (CCL) method can provide ideal slant total electron content (STEC) observables for establishing ionospheric models, it must rely on dual-frequency (DF) receivers, which results in high hardware costs. In this study, an ionosphere-weight (IW) single-frequency (SF) precise point positioning (PPP) method for extracting STEC observables is proposed, and multi-global navigation satellite system (GNSS)-integrated processing is adopted to improve the spatial resolution of the ionospheric model. To investigate the advantages of this novel method, 41 European stations are used to establish the regional ionospheric model, and both low- and high-solar-activity conditions are considered. The results show that the IW SFPPP-derived regional ionospheric model has a significantly better quality of vertical total electron content (VTEC) than the CCL method when using the final global ionospheric map (GIM) as a reference, especially in areas with sparse monitoring stations. Compared with the CCL method, the RMS VTEC accuracy of the IW SFPPP method can be improved by 17.4% and 12.7% to 1.09 and 2.83 total electron content unit (TECU) in low- and high-solar-activity periods, respectively. Regarding GNSS carrier-phase-derived STEC variation (dSTEC) as the reference, the dSTEC accuracy of the IW SFPPP method is comparable to that of the CCL method, and its RMS values are about 1.5 and 2.8 TECU in low- and high-solar-activity conditions, respectively. This indicates that the proposed method using SF-only observations can achieve the same external accord accuracy as the CCL method in regional ionospheric modeling.

The real-time precise point positioning (RT-PPP) service of BDS-3 (PPP-B2b) was launched in July 2021 to deliver a decimeter-level positioning service for users in China and its surrounding areas. During the use of PPP-B2b, we identified two issues adversely affecting positioning performance: systematic jumps in GPS clock bias and inaccuracies of the URA parameter. This paper analyzes the impact of these issues on PPP performance and proposes practical solutions. To address the GPS clock bias, we introduce three user-side strategies, including a jump detection and clock splicing method combined with various inter-system bias (ISB) estimation strategies. Performance comparisons reveal that effective bias processing can reduce the three-dimensional root-mean-square error (RMSE) from 0.83 to 0.08 m in kinematic mode. Additionally, we validate the insufficient accuracy of broadcasted URA and propose an approach to re-estimate the phase URA using data from over 50 monitoring stations across China, demonstrating that the refined URA enhances the PPP-B2b positioning stochastic model and this provides a viable direction for optimizing the PPP-B2b service.

The artificial intelligence (AI) weather forecast foundation models can infer and generate precise global atmospheric state forecasts on the user’s device and with speed over 10,000 times faster than the operational Integrated Forecasting System (IFS), and it is making increasingly significant contributions to geodetic applications represented by the Global Navigation Satellite System (GNSS). However, existing studies on the investigation of these AI models are typically carried out by concentrating on specific one or several meteorological events in certain regions or by comparison with physical models, and the evaluation results obtained in this manner are not comprehensive and universal. Additionally, we find that the results obtained by the foundation models through the “rollout” method for forecasting are not uniform in terms of time and space. This temporal and spatial inhomogeneity of accuracy and accuracy degradation are related to AI algorithms and attributes of training data, etc., but these characteristics have not been thoroughly explored and analyzed. In this study, we obtained the global forecast results of foundation models for 2022 and subsequently derived the GNSS tropospheric delay through numerical integration. We calculated the mean deviation, mean absolute error, and root mean square error of these data. Using these metrics, we analyzed the spatiotemporal inhomogeneity in the accuracy degradation of foundation models, represented by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We evaluated how this inhomogeneity changes with forecast time and identified the best-performing models across different regions and forecast durations. From the results, we find that taking topography into account when training the model enhances its accuracy at high altitudes, and the facilitating influence between the high related atmospheric variables such as precipitation and water vapor. The contributions of this study are twofold: it serves as a valuable reference for geodetic and remote sensing users employing foundational models, and offers insights and case supports for AI practitioners aiming to develop more accurate models for weather forecasting.

Ray-tracing through numerical weather models (NWMs) is one of the most accurate methods for determining slant tropospheric delays (STDs) in microwave remote sensing. However, the massive data volumes of high-resolution NWMs create substantial I/O operations, limiting large-scale ray-tracing on general hardware. This constraint has historically necessitated parameterized tropospheric delay models, which are disseminated as standardized products (e.g., zenith delays with mapping functions and horizontal gradients). Recently, the AI-driven VAEformer algorithm revolutionized NWM compression, achieving >470:1 ratios by compressing 37 pressure level, 0.25(degrees) x 0.25(degrees) fifth-generation ECMWF atmospheric reanalysis (ERA5) data into files smaller than surface-only VMF3 products (1(degrees) x 1(degrees) resolution). This breakthrough challenges the conventional reliance on parameterized models as the sole practical solution. We quantified discrepancies in tropospheric delay parameters between original ERA5 and variational autoencoder transformer (VAEformer)-compressed extreme compression of ERA5 (CRA5) data across 2022, evaluating compression fidelity on global grids and against in situ zenith tropospheric delay (ZTD) estimates. Results show global average precision loss from compression is <2 mm (<5%) for ZTD, with RMSE differences <0.2 mm when validated against over 5000 global navigation satellite system (GNSS) stations. These errors are significantly smaller than interanalysis center (AC) variations (4-6 mm) and GNSS-NWM mismatches (>10 mm). Our findings demonstrate CRA5 as a reliable ERA5 substitute, with compression-induced inaccuracies being negligible for most microwave-based remote sensing applications. This work underscores that parameterized delay modeling is no longer the exclusive pathway, enabling efficient local computation of high-precision STDs without through mapping functions and gradients.

High-quality precipitable water vapor (PWV) plays a vital role in climate change and weather prediction studies. This research introduces a novel scheme for retrieving high-resolution surface-domain PWV with real-time and forecasting capabilities with global coverage, utilizing weather forecast foundation models represented by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. The accuracy of the new scheme is cross-validated against PWVs from radiosondes, Global Navigation Satellite Systems (GNSSs), and the fifth generation ECMWF reanalysis (ERA5). Results show the new scheme achieves 3.01 mm global root mean square error in real time, and the value reduce to 2.25 mm when focusing only on land areas, which is more accurate than most existing methods that rely on postprocessed surface-domain data. The poor accuracy in low-latitude and midlatitude ocean regions limits the accuracy of the new scheme and future integration of GNSS PWV data from ocean sources is expected to improve it. Overall, the proposed scheme demonstrates very satisfactory global PWV accuracy and has the potential for further improvement with the development of artificial intelligence.

Accurate modeling of tropospheric delay is important for high-precision data analysis of space geodetic techniques, such as the Global Navigation Satellite System (GNSS). Empirical tropospheric delay models provide zenith delays with an accuracy of 3 to 4 cm globally and do not rely on external meteorological input. They are thus important for providing a priori delays and serving as constraint information to improve the convergence of real-time GNSS positioning, and in the latter case proper weighting is critical. Currently, empirical tropospheric delay models only provide delay values but not the uncertainty of delays. For the first time, we present a global empirical tropospheric delay model, which provides both the zenith delay and the corresponding uncertainty, based on 10 years of tropospheric delays from numerical weather models (NWMs). The model is based on a global grid and, at each grid point, a set of parameters that describes the delay and uncertainty in the constant, annual, and semiannual terms. The empirically modeled zenith delay has agreements of 36 and 38 mm compared to 3-year delay values from the NWM and 4-year estimates from GNSS stations, which is comparable to previous models such as Global Pressure and Temperature 3 (GPT3). The modeled zenith tropospheric delay (ZTD) uncertainty shows a correlation of 96 % with the accuracy of the empirical ZTD model over 380 GNSS stations over the 4 years. For GNSS stations where the uncertainty annual amplitude is larger than 20 mm, the temporal correlation between the formal error and smoothed accuracy reaches 85 %. Using GPS observations from ∼ 200 globally distributed IGS stations processed in kinematic precise point positioning (PPP) mode over 4 months in 2020, we demonstrate that using proper constraints can improve the convergence speed. The formal error modeling is based on a similar dataset to that of the GPT series, and thus it is also applicable for these empirical models.

Through the analysis of the coordinate time series of the stations in the Crustal Movement Observation Network of China (CMONOC) from 2011 to 2019, it is found that the daily network scatters of the filtered residuals (called as SFR) of the station coordinate time series have obvious seasonal variation characteristics which reaches a maximum during summer and a minimum during winter. This phenomenon has not yet been further explained. Our analysis results demonstrate that the seasonal pattern of the daily SFR solutions is the temperature-dependent, such as small-scale atmospheric perturbation. The pattern of SFR series of the difference between the zenith wet delay (ZWD) calculated with GPS data and that calculated with meteorological data is similar to the SFR solutions computed using GPS data. The results mean that the estimated ZWD using GPS data does not fully capture the total real atmospheric variation. The atmospheric delay model in current GNSS analysis can effectively solve large-scale atmospheric delay estimation, but it may fail to deduct certain small-scale atmospheric disturbances. We think that a considerable portion of these small-scale disturbances are absorbed by the estimates of station positions. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Precise Point Positioning (PPP) provides static positioning at the millimeter level and kinematic positioning ranging from millimeters to decimeters globally. Unlike the traditional network solution, PPP does not require data from other reference stations. This flexibility enhances the convenience of densifying the reference frame while maintaining the accuracy of solutions. In this study, Precise Point Positioning with Ambiguity Resolution (PPP-AR) was employed instead of a network solution, utilizing the combined orbit, clock, and bias products from IGS Repro3 to resolve the long-term station coordinates and derive their velocities, thereby contributing to the maintenance and densification of the terrestrial reference frame. We selected 46 globally distributed stations and performed PPP-AR over a 5-year period, from 2015.0 to 2020.0. The results show that differences in station coordinates between PPP-AR and IGS Repro3 are almost within 2 mm in the horizontal direction and within 5 mm in the vertical direction after Helmert transformation, which is roughly equivalent to the formal error of IGS solutions. The velocity uncertainty of PPP-AR solutions and the difference between PPP-AR and IGS Repro3 are nearly equal to the formal error of the ITRF horizontal velocity field and slightly exceed that of the IGS horizontal velocity field. The seasonal amplitudes of the remaining stations demonstrate strong consistency. Compared to PPP solutions, PPP-AR solutions provide improved coordinate and velocity precision, particularly in the east component. The consistency between the IGS Repro3 orbit/clock combination and IGS Repro3 position solutions is relatively high. These findings indicate that the PPP-AR technique can derive high-precision station coordinates with a similar level of accuracy to network solutions for supporting the maintenance and densification of the terrestrial reference frame.

The BeiDou Global Navigation Satellite System (BDS-3) started providing fully operational global services on July 31, 2020. It provides global positioning, navigation and timing (PNT) services and serves as key infrastructure for the determination and maintenance of the spatiotemporal reference frame in China. Challenges in the production of the spatiotemporal parameters of BDS exist due to its unique hybrid satellite constellation and regional-tracking-only capability. Innovative approaches are applied in the BDS ground control segment to ensure its high accuracy, high availability and high reliability. This paper introduces parts of these innovative approaches and discusses some key concepts of the ongoing comprehensive PNT system, which is regarded as the next-generation of BDS.

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The pseudolite positioning system can enhance Global Navigation Satellite System (GNSS) by improving satellite geometry and providing independent services in environments where GNSS is unavailable. This paper investigates a GNSS/Pseudolite integrated positioning system and verifies its positioning service performance in urban canyon environments. The experimental results indicate that in challenging urban canyon environments, the number of visible satellites for the GNSS significantly decreases, leading to a substantial decline in positioning accuracy. The GNSS/Pseudolite integrated system demonstrates its advantages. Even in favorable experimental conditions, compared to using GNSS PPP alone, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by 10% and 3D accuracy by 12%. When the obstruction is severe, such as when the cut-off elevation angle is 50 degrees, the GNSS/Pseudolite integrated PPP improves horizontal accuracy by 72.5% and 3D accuracy by 79.2% compared to GNSS PPP. In challenging urban canyon environments, the GNSS/Pseudolite integrated system can still provide high-precision positioning services.

Cuticular wax (CW) is the first defensive barrier of plants that forms a waterproof barrier, protects the plant from desiccation, and defends against insects, pathogens, and UV radiation. Sorghum, an important grass crop with high heat and drought tolerance, exhibits a much higher wax load than other grasses and the model plant Arabidopsis. In this study, we explored the regulation of sorghum CW biosynthesis using a bloomless mutant. The CW on leaf sheaths of the bloomless 41 (bm41) mutant showed significantly reduced very long-chain fatty acids (VLCFAs), triterpenoids, alcohols, and other wax components, with an overall 86% decrease in total wax content compared with the wild type. Notably, the 28-carbon and 30-carbon VLCFAs were decreased in the mutants. Using bulk segregant analysis, we identified the causal gene of the bloomless phenotype as a leucine-rich repeat transmembrane protein kinase. Transcriptome analysis of the wild-type and bm41 mutant leaf sheaths revealed BM41 as a positive regulator of lipid biosynthesis and steroid metabolism. BM41 may regulate CW biosynthesis by regulating the expression of the gene encoding 3-ketoacyl-CoA synthase 6. Identification of BM41 as a new regulator of CW biosynthesis provides fundamental knowledge for improving grass crops’ heat and drought tolerance by increasing CW.