Accurate astrometric measurements for star-forming regions located on the far side of the Milky Way remain scarce. In this work, we present the astrometric results for a 22 GHz water maser associated with star-forming region G040.96+02.48 located on the far side of the Milky Way, using the East Asian Very Long Baseline Interferometry Network. The target water maser's proper motion was determined to be ( mu alpha cos delta,mu delta ) = ( -2.06-0.51+0.53 , -2.95-0.44+0.45 ) mas yr-1. The derived three-dimensional kinematic distance to the star-forming region is 20.2 +/- 3.2 kpc, placing it slightly outside the Outer Scutum-Centaurus Arm. The corresponding vertical height of 872 +/- 139 pc indicates a significant warp of the outer Galactic disk, which is in good agreement with the latest precessing warp model. Moreover, the resulting peculiar motions reveal a complex kinematic pattern, characterized by a large outward radial velocity of -32 +/- 18 km s-1. Our observations substantially expand the valuable sample of star-forming regions with accurate astrometric measurements in the Extreme Outer Galaxy.

Universal Time (UT1) is a core component of the Earth orientation parameters (EOP). High-precision UT1 predictions are essential for satellite navigation, deep-space exploration, and the maintenance of national standard time. Although effective angular momentum (EAM) information can improve UT1 predictions, the impacts of different angular momentum combinations on prediction performance have not yet been systematically investigated. To improve the prediction accuracy of the National Time Service Center (NTSC) UT1 products, we constructed four prediction schemes: Case 1 uses only atmospheric angular momentum (AAM) data; Case 2 uses AAM + oceanic angular momentum (OAM) data; Case 3 uses AAM + OAM + hydrological angular momentum (HAM) data; and Case 4 uses the full EAM datasets combining AAM, OAM, HAM, and sea-level angular momentum (SLAM) data. The input UT1 series is from the NTSC EOP products, and the 10-day angular momentum forecasts are provided by the German Research Centre for Geosciences (GFZ). The rolling forecast evaluation was conducted from June 2024 to September 2025. The results show that Case 2 performs best for short-term UT1 predictions over 1–12 days, improving the mean prediction accuracy by 10.7%, 10.0%, and 52.0% relative to the predictions using Case 4, IERS finals.daily, and the original NTSC predictions, respectively. For medium- and long-term UT1 predictions over 13–90 days, Case 1 performs best, with corresponding mean improvements of 9.8%, 50.7%, and 61.3%, respectively. These results indicate that incorporating more angular momentum components does not necessarily lead to better UT1 predictions, i.e., Case 2 is preferable for short-term UT1 predictions, whereas Case 1 is more suitable for medium- and long-term UT1 predictions. These findings provide empirical evidence and practical guidance for optimizing UT1 prediction models.

Universal Time (UT1), a fundamental component of Earth Orientation Parameters (EOPs), is critical for satellite navigation, deep space exploration, and precise timekeeping. To establish an independent and controllable UT1 data service, the National Time Service Center (NTSC) of the Chinese Academy of Sciences has developed China’s first operational high-precision UT1 product, achieving full autonomy from data acquisition to dissemination. This paper details the system architecture, technical workflow, accuracy evaluation, and service applications of the product. By integrating multi-source observational data—including domestic Very Long Baseline Interferometry (VLBI), the International GNSS Monitoring and Assessment System (iGMAS), and Digital Zenith Telescope (DZT) measurements—and employing an adaptive weight combination strategy along with a weighted least-squares plus autoregressive (WLS+AR) prediction algorithm, we produce a comprehensive suite of UT1 final, rapid, and prediction products. Evaluated against the International Earth Rotation and Reference Systems Service (IERS) 20C04 series, the final UT1 product achieves an accuracy of 51.7 µs; the rapid product matches IERS accuracy while outperforming it in Length of Day (LOD) estimation; the 30-day prediction accuracy reaches 3.54 ms; and the real-time service offers an accuracy of 121.5 µs (compared to 137.3 µs for the IERS counterpart). The product is now officially operational and distributed through the NTSC website and iGMAS platform, providing independent, high-quality UT1 data for Earth science research and major national projects in China. This accomplishment marks a pivotal transition from international reliance to autonomous capability in this strategically vital field.

Abstract Polar motion (PM), a critical element of Earth Orientation Parameters (EOP), is essential for high‐precision applications such as deep space exploration and satellite navigation. Recent advances in prediction methods utilizing Effective Angular Momentum (EAM) data have become a key pathway for enhancing PM forecast accuracy. Focusing on two operational EAM forecast products—the 14‐day product from the Swiss Federal Institute of Technology Zurich (ETH Zurich) and the 10‐day product from the German Research Centre for Geosciences (GFZ)—this study systematically evaluates their performance differences and optimal application scenarios in PM prediction over the period from May 2023 to December 2024. Our analysis examines both the intrinsic accuracy of each product and the resulting accuracy evolution of PMX and PMY predictions. The results reveal complementary strengths: GFZ's product achieves higher accuracy in short‐term forecasts, whereas ETH's product demonstrates superior performance in medium‐to‐long‐term predictions. Specifically for PMX, ETH's product outperforms GFZ's across all 1–90 day lead times, with a maximum improvement of 13.84% and an average gain exceeding 8%; compared to concurrent International Earth Rotation and Reference Systems Service (IERS) forecasts, the improvement reaches up to 43.43%. For PMY, GFZ's product is more accurate at shorter lead times (1–13 days), while ETH's performs better beyond 14 days, notably outperforming the IERS Daily product by up to 35.45% within 31 days. This research provides a scientific basis for selecting EAM data in PM prediction, offering valuable insights for optimizing PM forecasting systems and supporting high‐precision remote sensing satellite missions.

We present GASV, a novel Python-based software package specifically designed for the analysis of Very Long Baseline Interferometry (VLBI) data. Developed with ease of installation and user-friendliness in mind, GASV supports both pipeline and interactive processing modes. The software processes VLBI baseline delays and rates in standard formats-such as HOPS outputs and NGS card files-to estimate key geodetic and astrometric parameters, including station coordinates, Earth Orientation Parameters, source coordinates, clock parameters, and atmospheric models. We evaluate the capabilities and performance of GASV, demonstrating that its parameter estimation accuracy for IVS INT, Regular, and CONT sessions is comparable to that achieved by the VLBI analysis centers at BKG and USNO. As a state-of-the-art tool, GASV not only enables high-quality single-session data processing but also supports global analyses of long-term SINEX files, generating Celestial Reference Frame and Terrestrial Reference Frame solutions with reliable accuracy.

Polar motion (PM), a key component of Earth orientation parameters (EOPs), is essential for high-precision satellite orbit determination and deep-space navigation. However, delays in data acquisition and processing limit its availability for real-time applications, necessitating the development of prediction models based on historical observations. Common approaches include least squares extrapolation (LS), autoregressive (AR) models, and their combination (LS + AR), often enhanced by effective angular momentum (EAM) from Earth’s fluid components. This study examines an EAM + LS + AR method for PM prediction, systematically evaluating how different EAM forecast horizons (1–10 days) affect 90-day prediction accuracy for both PM X and Y components. A segmented optimization strategy is proposed and validated against International Earth Rotation and Reference Systems Service (IERS) official predictions using the IERS EOP 14 C04 product. Key findings include: (a) Adjusting the EAM horizon substantially reduces prediction errors. Segmented prediction improves PM X accuracy by 20–30% (1–60 days) and 10–20% (61–90 days) relative to IERS rapid products, while PM Y short-term accuracy improves by 20–40% (1–15 days). (b) The influence of EAM horizon on long-term PM Y prediction gradually weakens, with errors converging to approximately 8 mas by day 90. (c) For 1–10-day forecasts, optimal horizons follow a systematic pattern: day m predictions achieve the highest accuracy using an (m−1)-day EAM horizon, while a 10-day horizon is optimal for long-term forecasts. (d) The proposed method shows clear advantages over IERS forecasts, with 83.5% of PM X predictions (1–90 days) and 50.78% of PM Y predictions (1–15 days), outperforming IERS daily products during the 2024 test period.

As an important component of Earth Orientation Parameters (EOP), the prediction of Celestial Pole Offsets (CPO) holds significant importance for missions such as deep space exploration. To explore a better CPO prediction algorithm that improves accuracy across different forecast spans, a CPO prediction algorithm is proposed based on a sliding window and bivariate least squares fitting. First, experiments determine an optimal sliding window of 900 days. Then, bivariate least squares fitting is performed on the selected 900-day historical data to complete extrapolation prediction. Then, bivariate least squares fitting is performed on the selected 900 day historical data to complete extrapolation prediction. Experimental results show that the proposed algorithm exhibits excellent accuracy. In comparisons with prediction results from participating teams in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), the algorithm's Mean Absolute Error (MAE) is superior to both ID154 and ID155. Team ID154 achieved the best dX prediction, while Team ID155 achieved the best dY prediction. Furthermore, the algorithm performs well not only on the EOP 14 C04 series but also on the newly released EOP 20 C04 series after the 2nd EOP PCC. Its prediction results are far better than those in the daily files published by the International Earth Rotation and Reference Systems Service (IERS). In terms of dX forecast accuracy, the MAE for the 10th, 30th, and 57th days were reduced by 53

Polar Motion (PM) is a crucial parameter linking the celestial and terrestrial reference frames. Integrating Earth's fluid effective angular momentum (EAM) and neural network is an effective way to improve PM prediction accuracy. However, EAM data is polluted by high-frequency noise and the neural network models are highly sensitive to the input EAM dataset. To improve PM prediction accuracy, this study proposes a method combining Complex Segmented Least Squares (CSLS) with Long Short-Term Memory (LSTM) using the noise-reduced EAM data and the filtered 6-day forecast EAM data. Geodetic angular momentum function is employed to reduce the high frequency noise signal of EAM, Kalman Filter is adapted to denoise the 6-day forecast product, and CSLS + Autoregression (AR) and CSLS + LSTM are used for fitting and prediction. The 6-day predicted outcomes with denoised EAM achieved the reductions of the Mean Absolute Error (MAE) in x direction by 48.68

Recent observations of Universal Time (UT1) indicate an acceleration in Earth's rotation. If sustained under the current leap-second framework, this behavior could eventually prompt consideration of a negative leap second. We examine whether the recent acceleration is consistent with an approximately 70-year, core-related modulation of length of day (LOD). After removal of modeled tidal, surface-fluid, and secular contributions, residual LOD contains a near-70-year component, and a similar component is present in core angular momentum (CAM)-derived equivalent LOD inferred from geomagnetic observations. All harmonic, spectral, and LOD-CAM analyses reported here use the common 1883–2022 interval. Harmonic regression over trial periods of 50–100 yr gives periods of 69.7 yr for residual LOD and 71.8 yr for CAM-derived equivalent LOD, with amplitudes of 2.87 and 1.94 ms, respectively. Lomb–Scargle spectra show peaks near 67.8 and 70.5 yr. The annual series have a zero-lag correlation of 0.918. Their lagged correlation has a broad maximum for a CAM lead of approximately 1-3 yr, with a numerical maximum of 0.932 at 2 yr. Because both records are strongly autocorrelated, these coefficients are used to characterize their correspondence rather than to assess predictive significance. The results are consistent with a core-related contribution to low-frequency rotational variability, but they do not uniquely separate the contributions of electromagnetic, topographic, gravitational, and viscous core–mantle coupling mechanisms. Within the fitted model, the multidecadal component alone does not indicate sustained near-term shortening of the day that would, by itself, require a negative leap second. This is a model-dependent geophysical assessment, not an operational prediction of future UTC adjustments.

Universal Time (UT1) is a key parameter characterizing Earth's rotation, and very long baseline interferometry (VLBI) is the mainstream technique for measuring UT1. To address the limitations in the timeliness and accuracy of existing global ionospheric models for single-frequency VLBI UT1 measurements, we construct a single-station regional ionospheric model using GNSS data from the VLBI stations on the Jilin-Kashi baseline. We apply this model to VLBI observations and compare its correction performance with that of a global predictive model and a global post-processed model. The results show that the line-of-sight ionospheric delays and baseline corrections calculated with the single-station regional model have precision close to that of the global post-processed model and are substantially better than those of the global predictive model. After correction with the single-station regional model, the derived UT1 values differ from the US Naval Observatory (USNO) reference values by a mean bias of -15.6 us and an RMS deviation of 82.3 us, both better than the results obtained with the other two model classes. A single-station regional ionospheric model constructed independently from GNSS data available at VLBI stations can effectively correct single-frequency VLBI observations and support quasi-real-time high-precision UT1 measurements. It therefore has important value for improving the timeliness of independent UT1 products.

Given that effective angular momentum (EAM) data demonstrate a strong correlation with length of day (LOD) data and are extensively utilized in the prediction of the universal time (UT1), this research integrated the EAM into the design of a Kalman filter. At the solution combination level, the UT1, LOD, and EAM were merged to derive a UT1/LOD sequence featuring higher accuracy and enhanced continuity. To begin with, a comprehensive evaluation of the three datasets was conducted to identify the systematic biases and periodic components of the LOD. Subsequently, geodetic angular momentum (GAM) data were employed to rectify the EAM data spanning from 2019 to 2022. Finally, the corrected EAM was combined with the UT1 and LOD through Kalman modeling. To evaluate the capability of this EAM-aided Kalman filter, Jet Propulsion Laboratory (JPL) and Wuhan University (WHU) LOD data, International Very Long Baseline Interferometry (VLBI) Service for Geodesy and Astrometry (IVS) intensive and National Time Service Center (NTSC) UT1 data, and German Research Centre for Geosciences (GFZ) EAM data were used for combination experiments. The final estimations of the UT1 and LOD were compared with the International Earth Rotation Service (IERS) Earth-orientation parameter (EOP) 20 C04 series. From July to September 2021, the root mean square (RMS) of the combined UT1 series was reduced from 38 µs to 26 µs for the IVS intensive UT1, with an improvement of 30%. The RMS of the combined UT1 series was reduced from 102 µs to 47 µs for the NTSC UT1 measurement, with an improvement of 54%. The bias of the LOD was effectively corrected and the RMS of the LOD improved by 60–70% and the standard deviation of the LOD improved by 11–30%. Further, the final estimated uncertainties of the UT1 and LOD are, in general, consistent with the estimated RMS, indicating a reasonable estimation of uncertainties. Comparative experiments with and without the EAM show that using EAM data can effectively reduce the extreme values, especially for the NTSC UT1 series with large uncertainties. In summary, this EAM-aided Kalman filter can produce UT1 and LOD series with improved accuracy, and with reasonable uncertainties.

In 2021, the International Earth Rotation and Reference Systems Service (IERS) established a working group tasked with conducting the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) to assess the current accuracy of EOP forecasts. From September 2021 to December 2022, EOP predictions submitted by participants from various institutes worldwide were systematically collected and evaluated. This article summarizes the campaign's outcomes, concentrating on the forecasts of the dX, dY, and dψ, dε components of celestial pole offsets (CPO). After detailing the campaign participants and the methodologies employed, we conduct an in-depth analysis of the collected forecasts. We examine the discrepancies between observed and predicted CPO values and analyze their statistical characteristics such as mean, standard deviation, and range. To evaluate CPO forecasts, we computed the mean absolute error (MAE) using the IERS EOP 14 C04 solution as the reference dataset. We then compared the results obtained with forecasts provided by the IERS. The main goal of this study was to show the influence of different methods used on predictions accuracy. Depending on the evaluated prediction approach, the MAE values computed for day 10 of forecast were between 0.03 and 0.16 mas for dX, between 0.03 and 0.12 mas for dY, between 0.07 and 0.91 mas for dψ, and between 0.04 and 0.41 mas for dε. For day 30 of prediction, the corresponding MAE values ranged between 0.03 and 0.12 for dX, and between 0.03 and 0.14 mas for dY. This research shows that machine learning algorithms are the most promising approach in CPO forecasting and provide the highest prediction accuracy (0.06 mas for dX and 0.08 mas for dY for day 10 of prediction). Graphical abstract

We report measurements of trigonometric parallax and proper motion for two 6.7 GHz methanol and two 22 GHz water masers located in the far portion of the Sagittarius spiral arm as part of the BeSSeL Survey. Distances for these sources are estimated from parallax measurements combined with three-dimensional kinematic distances. The distances of G033.64−00.22, G035.57−00.03, G041.15−00.20, and G043.89−00.78 are 9.9 ± 0.5, 10.2 ± 0.6, 7.6 ± 0.5, and 7.5 ± 0.3 kpc, respectively. Based on these measurements, we suggest that the Sagittarius arm segment beyond about 8 kpc from the Sun in the first Galactic quadrant should be adjusted radially outward relative to previous models. This supports the suggestion of Xu et al. that the Sagittarius and Perseus spiral arms might merge in the first quadrant before spiraling inward to the far end of the Galactic bar.

Predicting Earth Orientation Parameters (EOP) is crucial for precise positioning and navigation both on the Earth’s surface and in space. In recent years, many approaches have been developed to forecast EOP, incorporating observed EOP as well as information on the effective angular momentum (EAM) derived from numerical models of the atmosphere, oceans, and land-surface dynamics. The Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) aimed to comprehensively evaluate EOP forecasts from many international participants and identify the most promising prediction methodologies. This paper presents the validation results of predictions for universal time and length-of-day variations submitted during the 2nd EOP PCC, providing an assessment of their accuracy and reliability. We conduct a detailed evaluation of all valid forecasts using the IERS 14 C04 solution provided by the International Earth Rotation and Reference Systems Service (IERS) as a reference and mean absolute error as the quality measure. Our analysis demonstrates that approaches based on machine learning or the combination of least squares and autoregression, with the use of EAM information as an additional input, provide the highest prediction accuracy for both investigated parameters. Utilizing precise EAM data and forecasts emerges as a pivotal factor in enhancing forecasting accuracy. Although several methods show some potential to outperform the IERS forecasts, the current standard predictions disseminated by IERS are highly reliable and can be fully recommended for operational purposes.

The Length of Day (LOD) and the Universal Time (UT1) play crucial roles in satellite positioning, deep space exploration, and related fields. The primary method for predicting LOD and UT1 is least squares fitting combined with autoregressive (AR) models. Polynomial Curve Fitting (PCF) has greater accuracy in capturing long-term trends compared to standard least squares fitting. In this study, PCF combined with Weighted Least Squares (WLS) is employed to fit and extrapolate the periodic and trend components of the LOD series after removing tidal influences. Additionally, considering the time-varying characteristics of the LOD series, a Long Short-Term Memory (LSTM) network is utilized to predict the residuals derived from the fitting process. The 14 C04 LOD series released by the International Earth Rotation and Reference System Service (IERS) is used as the base series, with 70 LOD and UT1-UTC prediction experiments conducted during the period from 1 September 2021–31 December 2022. The results indicate that the PCF+WLS+LSTM method is well-suited for medium- and long-term (90–360 days) prediction of the LOD and UT1-UTC. Significant improvements in prediction accuracy were obtained for periods ranging from 90–360 days, particularly beyond 150 days, where the average accuracy improved by over 20% compared to IERS Bulletin A. Specifically, the largest prediction accuracy increase for LOD and UT1-UTC was 49.5% and 59.2%, respectively.

AbstractGrowing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. Considering these developments and recently emerged new methodologies, the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) was pursued in 2021–2022. The campaign was led by Centrum Badań Kosmicznych Polskiej Akademii Nauk in cooperation with Deutsches GeoForschungsZentrum and under the auspices of the International Earth Rotation and Reference Systems Service. This paper provides the analysis and evaluation of the polar motion predictions submitted during the 2nd EOP PCC with the prediction horizons between 10 and 30 days. Our analysis shows that predictions are highly reliable with only a few occasional discrepancies identified in the submitted files. We demonstrate the accuracy of EOP predictions by (a) calculating the mean absolute error relative to polar motion observations from September 2021 through December 2022 and (b) assessing the stability of the predictions in time. The analysis shows unequal results for the x and y components of polar motion (PMx and PMy, respectively). Predictions of PMy are usually more accurate and have a smaller spread across all submitted files when compared to PMx. We present an analysis of similarity between the participants to indicate what methods and input data give comparable output. We also prepared the ranking of prediction methods for polar motion summarizing the achievements of the campaign.

Studying stars that are located off the Galactic plane is important for understanding the formation history of the Milky Way. We searched for SiO masers toward off-plane O-rich asymptotic giant branch (AGB) stars from the catalog presented by Mauron et al. (2019) in order to shed light on the origin of these objects. A total of 102 stars were observed in the SiO $J$=1-0, $v=1$ and 2 transitions with the Effelsberg-100 m and Tianma-65 m telescopes. SiO masers were discovered in eight stars, all first detections. The measured maser velocities allow the first estimates of the host AGB stars' radial velocities. We find that the radial velocities of three stars (namely G068.881-24.615, G070.384-24.886, and G084.453-21.863) significantly deviate from the values expected from Galactic circular motion. The updated distances and 3D motions indicate that G068.881$-$24.615 is likely located in the Galactic halo, while G160.648-08.846 is probably located in the Galactic thin disk, and the other six stars are probably part of the Galactic thick disk.

基于干涉时差测量的卫星无源测定轨是一种有巨大发展潜力的测定轨方法,各测站使用网络传输观测数据至相关中心,再通过干涉测量手段测量卫星信号至各测站的时间差值,最后使用时差数据进行卫星定轨.为了验证基于干涉时差测量的卫星无源测定轨的定轨精度,在北京、喀什、深圳、哈尔滨搭建实验网,对GEO(geosynchronous Erath orbit)卫星亚太 6C 进行干涉时差测量测定轨,观测时长为 9天.对时差数据、定轨残差和重叠弧段轨道差的分析表明,干涉时差测量测定轨的时差测量的精度约为 0.7 ns,定轨残差的RMS(root mean square)优于 0.7 m,定轨精度(重叠弧段轨道误差)为 17.78 m.对轨道误差源进行了分析,并根据误差源提出了后续进一步提高轨道精度的几种方法.

With precise orbits of GEO satellites, the precise time transfer between stations can be realized by VLBI observations of GEO satellite. This can be a new time transfer method independent of GNSS co-view, and can obtain same or even higher time transfer accuracy.In this paper, a VLBI time transfer method based on GEO satellite observation is proposed. The time transfer measurement model is established, and the major systematic errors and correction methods are studied. The precise orbits of GEO satelltes which are critical for VLBI measurement model, are determined by GNSS and by the method of Orbit Determination Transfer Tracking (ODTT). The VLBI time transfer experiment was carried out by observing Beidou GEO satellite using Jilin and Sanya stations of the National Time Service Center (NTSC) VLBI network. The GNSS PPP time transfer results are used as a standard for evaluation. The results show that the consistency between VLBI time transfer and GNSS PPP time transfer is consistent within 2 nanoseconds.Here, we present the preliminary experimental results. Further improvements include: Selection of setellites with larger bandwidth to improve the time transfer accuracy; the correction of satellite antenna phase center to satellite center of mass; the geometric tide correction of stations and other error sources should be investigated in the future.