In this study, we investigate the performances of a commercial Global Navigation Satellite System (GNSS) Radio Occultation (RO) mission and a new-generation RO constellation, i.e., Spire and Constellation Observing System for Meteorology, Ionosphere, and Climate 2 (COSMIC-2), respectively. In the statistical comparison between Spire and COSMIC-2, the results indicate that although the average signal-to-noise ratio (SNR) of Spire is far weaker than that of COSMIC-2, the penetration of Spire is comparable to, and occasionally even better than, that of COSMIC-2. In our analysis, we find that the penetration depth is contingent upon various factors including SNR, GNSS, RO modes, topography, and latitude. With the reanalysis of the European Centre for Medium-Range Weather Forecasts and Radiosonde as the reference data, the identical error characteristics of Spire and COSMIC-2 reveal that overall, the accuracy of Spire’s neutral-atmosphere data products was found to be comparable to that of COSMIC-2.

The accurate determination of Atmospheric Boundary Layer Height (ABLH) is crucial in various atmospheric studies and practical applications. In this study, we present a comprehensive comparative analysis of five distinct methods for estimating ABLH using Global Navigation Satellite System (GNSS) Radio Occultation (RO) data. These methods encompass the use of bending angle and refractivity profiles, namely Minimum Gradient methods of the Bending Angle (MGBA) and Refractivity (MGR) profiles, breaking point, Wavelet Covariance Transform (WCT), and Double-Parameter Model Function (DPMF). GNSS-RO data from COSMIC-2 and Spire are used. To establish robust validation, radiosonde data are employed as a reference, ensuring the reliability of our findings. The results reveal notable variations in the performances of these ABLH estimation methods. Specifically, the MGBA, MGR, breaking point, and DPMF methods exhibit strong correlations with the reference data. Conversely, the WCT method displays weaker correlations, higher biases, and elevated root-mean-square-errors, suggesting limitations in capturing the true ABLH. Furthermore, we remove outlier screening to facilitate a comparison of the differences among the five methods. The WCT and DPMF methods can detect strong variations in the profiles near the Earth’s surface and consider them as ABLH. However, these variations are caused by errors. The MGBA method emerges as a reliable and stable option, while the WCT and DPMF methods should be used with caution due to the lower quality of the GNSS-RO profiles near the Earth’s surface.

Currently, ground-based global navigation satellite system (GNSS) techniques have become widely recognized as a reliable and effective tool for atmospheric monitoring, enabling the retrieval of zenith total delay (ZTD) and precipitable water vapor (PWV) for meteorological and climate research. The International GNSS Service analysis centers (ACs) have initiated their third reprocessing campaign, known as IGS Repro3. In this campaign, six ACs conducted a homogeneous reprocessing of the ZTD time series spanning the period from 1994 to 2022. This paper primarily focuses on ZTD products. First, the data processing strategies and station conditions of six ACs were compared and analyzed. Then, formal errors within the data were examined, followed by the implementation of quality control processes. Second, a combination method is proposed and applied to generate the final ZTD products. The resulting combined series was compared with the time series submitted by the six ACs, revealing a mean bias of 0.03 mm and a mean root mean square value of 3.02 mm. Finally, the time series submitted by the six ACs and the combined series were compared with VLBI data, radiosonde data, and ERA5 data. In comparison, the combined solution performs better than most individual analysis centers, demonstrating higher quality. Therefore, the advanced method proposed in this study and the generated high-quality dataset have considerable implications for further advancing GNSS atmospheric sensing and offer valuable insights for climate modeling and prediction.

Real-time kinematic orbit determination (KOD) based on broadcast ephemerides (BRD) enables onboard, high-rate orbit updates for Low Earth Orbit (LEO) missions but with limited accuracy. With the operational availability of the Galileo High Accuracy Service (HAS) and the BDS-3 Precise Point Positioning B2b (PPP-B2b), real-time, high-accuracy corrections have become accessible to spaceborne receivers, enhancing orbit determination performance. Nevertheless, each product exhibits distinct strengths and limitations: BRD provides global coverage at relatively low accuracy; HAS delivers global corrections but exhibits degraded performance in the Asia–Pacific; PPP-B2b achieves high accuracy but is confined to a regional service centered on China. These complementary characteristics motivate their integration to achieve both high precision and robust global performance. Using the LuTan-1A mission as a case study, we assess the quality and availability of HAS and PPP-B2b products and propose a real-time KOD method that integrates BRD, HAS and PPP-B2b. Carrier phase pseudo-ambiguities are modeled as random-walks, with noise parameters determined for each ephemeris source through a para metric search experiment. Results show that the three-dimensional orbit error decreases from 25.06 cm for the combination of GPS and BDS-3 BRD to 18.86 cm when HAS is incorporated, and further to 17.09 cm with the addition of PPP-B2b. Specially, in the Asia-Pacific region, adding PPP-B2b to BRD+HAS reduces the orbit error from 20.57 cm to 16.63 cm. Overall, these findings demonstrate the stability and adaptability of the proposed integrated method under current GNSS configurations, providing a practical pathway to higher-precision, real-time orbit determination for future LEO missions.

The quality of Global Navigation Satellite System (GNSS) observations on smartphones is highly susceptible to multipath and non-line-of-sight (NLOS) effects in urban environments, resulting in complex and highly variable observation errors. These challenges highlight the necessity of a reliable stochastic model to ensure robust and unbiased parameter estimation. However, conventional empirical stochastic models, such as elevation-dependent or signal-to-noise ratio (SNR)-based weighting schemes, are often insufficient to capture the rapidly changing stochastic behavior of observations in dense urban environments. To overcome this limitation, an adaptive GNSS stochastic model based on a deep neural network (DNN) is developed by integrating SNR, satellite elevation angle, and post-fit pseudorange residuals, which provide a strong indicator of observation quality and environmental context. Specifically, a fully connected DNN is designed to use SNR, satellite elevation angle, and post-fit pseudorange residual as input features, representing signal strength, satellite geometry, and residual information, respectively, and to learn their nonlinear relationship with measurement uncertainty. The network output is then used to adaptively update the diagonal elements of the measurement noise covariance matrix, thereby realizing epoch-wise adaptive weighting within the Kalman filtering process. The proposed DNN-based stochastic model, together with several conventional models, was evaluated using GNSS observations collected by a low-cost u-blox ZED-F9P receiver (u-blox AG, Thalwil, Switzerland) and a Samsung Galaxy S21+ smartphone (Samsung Electronics Co., Ltd., Suwon, Republic of Korea) during vehicle experiments in dense urban canyons. The code-based single point positioning (SPP) results demonstrate that the DNN-based model consistently outperforms traditional stochastic models under both open-sky and urban conditions. The improvement is particularly pronounced for smartphone observations in severely obstructed environments. The proposed DNN-based model reduces the 3D RMSE from 14.25 m, 13.68 m, and 13.05 m, obtained with the elevation-, SNR-, and integrated elevation–SNR-based models, respectively, to 8.94 m, representing an improvement of approximately 35%. A similar improvement is observed for the u-blox ZED-F9P receiver, where the 3D RMSE decreases from 5.71 m, 4.69 m, and 5.15 m to 3.10 m. These results suggest the effectiveness of the proposed DNN-based stochastic model in mitigating complex observation errors and improving positioning accuracy, providing a promising solution for reliable positioning of low-cost GNSS receivers in challenging urban environments.

Commercial SmallSats offer cost-effective alternatives to traditional GNSS radio occultation (GNSS-RO) missions through scalable constellation deployments. In GNSS-RO processing, the short-term stability of the LEO receiver clock is a key constraint on the feasibility and accuracy of undifferenced (UD) retrievals. Specifically, limited stability in compact receivers can introduce clock noise that degrades retrieval profiles and increases uncertainty. This study evaluated Spire and PlanetiQ onboard clock stability and quantified the impact on bending-angle and refractivity retrievals. Spire exhibited lower short-term clock stability, with 1-s clock stability exceeding $10^{-6}$, making UD infeasible, while its single-differenced (SD)-derived profiles remained consistent with UCAR and ECMWF reference datasets. In contrast, PlanetiQ exhibited better short-term stability, with the 1-s clock stability typically better than $10^{-9}$. Subsequent analyses were confined to clock segments with 1-s clock stability better than $10^{-12}$, sufficient for accurate SD and UD processing. For PlanetiQ, refractivity derived from both methods was in agreement between 10 and 25 km (mean bias $< 0.05\%$, standard deviation $< 1\%$); above 25 km, SD showed slightly larger deviations due to reference-link noise. Across constellations, GPS showed the lowest deviations while GLONASS had the highest. Sensitivity tests with injected clock noise targeting 1-s clock stability over the range of $10^{-12}$ to $10^{-10}$ showed that UD and SD were statistically comparable when 1-s clock stability was about $3.0\times 10^{-11}$, and the retrieval deviations increased with both altitude and noise amplitude. These results confirm that PlanetiQ's high clock stability supports accurate SD and UD retrievals and provide valuable insights for oscillator selection, quality control, and processing strategy in cost-effective GNSS-RO missions.