ALBERT

Description: Introducing precise atmosphere information into precise point positioning enables rapid ambiguity resolution and introduces a significant accuracy improvement. However, it can only be implemented in regions with dense networks and stable communication links. For larger areas, e.g., an intercontinental level, there is a conflict between the accuracy of corrections and the amount of atmosphere information to be disseminated. We develop a hierarchical augmentation mode to combine the advantages of the fitting model and region interpolation model to relieve the communication burden. Relying on the fitting model with fewer coefficients applied over large areas as the essential information, the unmodeled errors are calculated at each reference station, and further correction information is optional compensation depending on the magnitude of the unmodeled residuals. We perform the proposed models on 103 EUREF Permanent Network stations with 200-km station spacing and 84 stations as the external validation. The ionosphere and troposphere fitting models have an average accuracy of about 4.2 and 1.3 cm, respectively, under meteorologically calm conditions. The unmodeled error transmission determined by the magnitude of residuals can be reduced by 61% and 96% for the ionospheric and tropospheric delays, respectively, with respect to the legacy interpolation mode. Further compensation implemented, i.e., unmodeled residuals, can achieve instantaneous convergence for 83.6% of all solutions, and the overall initialization time is within 1.0 min. Thus, the proposed hierarchical positioning mode satisfies real-time positioning convergence requirements and significantly reduces massive corrections in communication.

Description: The performance of precise point positioning (PPP) has been significantly improved thanks to the continuous improvements in satellite orbit, clock, and ambiguity resolution (AR) technologies, but the convergence speed remains a limiting factor in real-time PPP applications. To improve the PPP precision and convergence time, tropospheric delays from a regional network can be modeled to provide precise correction for users. We focus on the precise modeling of zenith wet delay (ZWD) over a wide area with large altitude variations for improving PPP-AR. By exploiting the water vapor exponential vertical decrease, we develop a modified optimal fitting coefficients (MOFC) model based on the traditional optimal fitting coefficients (OFC) model. The proposed MOFC model provides a precision better than 1.5 cm under sparse inter-station distances over the Europe region, with a significant improvement of 70% for high-altitude stations compared to the OFC model. The MOFC model with different densities of reference stations is further evaluated in GPS and Galileo kinematic PPP-AR solutions. Compared to the PPP-AR solutions without tropospheric delay augmentation, the positioning precision of those with 100-km inter-station spacing MOFC and OFC is improved by 25.7% and 17.8%, respectively, and the corresponding time to first fix (TTFF) is improved by 36.9% and 33.0% in the high-altitude areas. On the other hand, the OFC model only slightly improves the TTFF and positioning accuracy when using the 200 km inter-station spacing modeling and even degrades the positioning for high-altitude stations, whereas using the MOFC model, the PPP-AR solutions always improve. Moreover, the positioning precision improvement of MOFC compared with OFC is about 22.1%, 21.7%, and 25.7% for the Galileo-only, GPS-only, and GPS + Galileo PPP-AR solutions, respectively.

Description: The inter-system bias (ISB) is an important parameter in multi-GNSS precise point positioning (PPP). However, on the one hand, the generation mechanism and error components of ISB are not clear. On the other hand, it is unclear whether the ISB parameter should be added to the BDS-2/BDS-3 combined PPP. First, in order to solve these problems, an extended ISB mathematical model is proposed, which unifies the common errors between receiver and satellite, and extends the original ISB model. Second, to demonstrate the correctness of the new model, the components of the new ISB model are verified, and then it is used to explain whether the ISB parameter should be added to BDS-2/BDS-3 combined PPP. Furthermore, 41 stations from the MGEX network and precise products from COD (Center for Orbit Determination in Europe), GBM (Deutsches GeoForschungsZentrum) and WUM (Wuhan University) are used to calculate and analyze the multi-GNSS PPP and ISB during day of year (DOY) 307–365, 2020. Finally, we propose to use a different estimation method of ISB with different precise products to improve the positioning accuracy and shorten the convergence time. The experimental results show that: (1) ISB parameter is composed of five parts: time system error, receiver hardware delay, signal distortion biases (SDB), MGEX-realized (multi-GNSS experiment) time scale and other unmodeled deviations. (2) Due to different receiver hardware delay, SDB and MGEX-realized time scale between BDS-2 and BDS-3, it is necessary to add an ISB parameter in BDS-2/BDS-3 PPP. (3) In multi-GNSS PPP, if the ISB changes greatly but the traditional constant method is used to estimate the ISB parameter, the impact on single station PPP coordinates can reach decimeters. The statistical results demonstrate that the RMS of GBM(CON(Constant)) in East (E), North (N) and Up (U) directions are 2.34 cm, 0.60 cm, and 1.59 cm, respectively, while the RMS of GBM(RWK(Random walk)) decreased by 59.8 %, 13.3 %, and 18.2 %, respectively.

Description: With the high-precision products of satellite orbit and clock, uncalibrated phase delay, and the atmosphere delay cor- rections, Precise Point Positioning (PPP) based on a Real-Time Kinematic (RTK) network is possible to rapidly achieve centimeter-level positioning accuracy. In the ionosphere-weighted PPP–RTK model, not only the a priori value of ionosphere but also its precision affect the convergence and accuracy of positioning. This study proposes a method to determine the precision of the interpolated slant ionospheric delay by cross-validation. The new method takes the high temporal and spatial variation into consideration. A distance-dependent function is built to represent the stochastic model of the slant ionospheric delay derived from each reference station, and an error model is built for each reference station on a five-minute piecewise basis. The user can interpolate ionospheric delay correction and the corresponding precision with an error function related to the distance and time of each reference station. With the European Reference Frame (EUREF) Permanent GNSS (Global Navigation Satellite Systems) network (EPN), and SONEL (Système d’Observation du Niveau des Eaux Littorales) GNSS stations covering most of Europe, the effective- ness of our wide-area ionosphere constraint method for PPP-RTK is validated, compared with the method with a fixed ionosphere precision threshold. It is shown that although the Root Mean Square (RMS) of the interpolated ionosphere error is within 5 cm in most of the areas, it exceeds 10 cm for some areas with sparse reference stations during some periods of time. The convergence time of the 90th percentile is 4.0 and 20.5 min for horizontal and vertical directions using Global Positioning System (GPS) kinematic solution, respectively, with the proposed method. This convergence is faster than those with the fixed ionosphere precision values of 1, 8, and 30 cm. The improvement with respect to the latter three solutions ranges from 10 to 60%. After integrating the Galileo navigation satellite system (Galileo), the convergence time of the 90th percentile for combined kinematic solutions is 2.0 and 9.0 min, with an improvement of 50.0% and 56.1% for horizontal and vertical directions, respectively, compared with the GPS-only solution. The average convergence time of GPS PPP-RTK for horizontal and vertical directions are 2.0 and 5.0 min, and those of GPS + Galileo PPP-RTK are 1.4 and 3.0 min, respectively.

Description: Ambiguity resolution (AR) is a core technology that helps to speed up convergence time and increase positioning accuracy for precise point positioning (PPP), and the performance of PPP-AR is based on the quality of ambiguity resolution products. Real-time PPP-AR becomes a reality as users can now obtain publicly accessible real-time observable-specific signal bias (OSB) products from the Centre National d’Etudes Spatiales (CNES). Therefore, an analysis of the quality of OSB products and an evaluation of the performance of PPP-AR are required to promote the application of real-time positioning. For a total of 31 days between day of year (DOY) 121 and 151 in 2021, observation data were collected from 90 stations, and the OSB products were used to assess the experiments. As for the quality of the OSB products, the data availability (DA) of the GPS and Galileo satellites was greater than 97%, whereas that of BDS was less than 60%; the maximum fluctuation value (MAX) and standard deviation (STD) of the GPS, Galileo, and BDS satellites were 0.045 and 0.012; 0.081 and 0.028; and 0.292 and 0.085 cycles, respectively. In terms of ambiguity residuals using the OSB products, the wide-lane (WL) residual percentages within ±0.25 cycles for the GPS, Galileo, BDS-2, and BDS-3 systems were more than 92%, and the narrow-lane (NL) residual percentages within ±0.25 cycles for the four systems were 92%, 89%, 79%, and 60%, respectively. With regard to the performance of PPP-AR, the GPS+Galileo solution showed the best performance in the kinematic positioning mode, in which the mean root mean square (RMS) of positioning accuracy was 1.06, 1.27, and 2.85 cm for the east (E), north (N), and up (U) components, respectively, and the mean convergence time reached 9.6 min. In the static positioning mode, the mean convergence times of the GPS-only and GPS+Galileo solutions were 11.4 min and 8.0 min, respectively, and both of their mean RMS for positioning accuracy reached 0.79, 0.95, and 1.48 cm for the E, N, and U components, respectively. However, the addition of BDS did not further enhance the performance of multi-GNSS PPP-AR in either the kinematic or static positioning mode due to the poor quality of the real-time BDS products. More importantly, a prediction method was proposed to avoid re-convergence and to enhance the reliability of PPP-AR in the event of short-time missing real-time OSB products and to improve the positioning accuracy and the ambiguity fixed rate.

Description: As wide-area atmosphere correction models often struggle to attain high-precision and uniformity, especially in regions experiencing rapid atmospheric changes, they are usually as constraints to the user-end atmospheric parameters. The proper constraint is crucial for rapid or instantaneous convergence in Precise Point Positioning with Ambiguity Resolution (PPP-AR). However, the empirical uncertainty information commonly adopted cannot accurately represent the actual quality of a real-time established model. To address this problem, we propose an approach to determine atmospheric uncertainty information using the residuals of the atmosphere delays fitting to a polynomial model. Two sets of 2°×2° grids with a 15-minute update rate are generated based on the fitting residuals of tropospheric and satellite-wise slant ionospheric delays derived using PPP-AR at all reference stations. The proposed approach is verified using 166 EUREF Permanent Network stations with a 150 km station-spacing and validated using 113 additional stations. The utilization of wide-area grid uncertainty information leads to an average convergence time that reduces to 1.5 min, i.e., improved by up to 36% compared to the legacy empirical constraints. Additionally, an improvement of 9% in the fixing rate compared to the empirical constraint solutions is observed. In conclusion, the provision of atmosphere uncertainty information accurately characterizes the performance of the fitting model in all coverage areas, resulting in rapid positioning performance with lower computational resources.

Description: Due to the spectrum congestion of current navigation signals in the L-band, it is difficult to apply for another two proper frequencies in this band for future low earth orbit (LEO)-based navigation augmentation systems. A feasible frequency scheme of using the combined frequencies in the L, S and C bands is proposed. A high-efficiency modulation scheme, termed continuous phase modulation, is adopted to make full use of the very limited spectrums and satisfy the radio frequency compatibility with the existing navigation systems, radio astronomy, and microwave landing systems. The high propagation loss in the S and C bands is absent for LEO, as the power margin owing to the short-distance propagation has compensated the frequency-dependent attenuation. Besides, for high-precision positioning, we consider the specific integer ratios between frequencies and propose a strategy for LEO precise point positioning (PPP) ambiguity resolution (AR) by directly fixing the L + S or L + C dual-band ionospheric-free (IF) ambiguity. Based on the simulated data, the quality of fractional cycle biases (FCBs) and the performance of PPP AR are analyzed. After removing the FCBs, 100.0, 99.7 and 71.7% of the fractional parts are within ± 0.15 cycles for GPS narrow-lane, LEO L + S dual-band IF and LEO L + C dual-band IF float ambiguities. At user stations, the convergence time of GPS PPP in static mode can be significantly shortened from 17.9 to within 2.5 min with the augmentation of 5.44 LEO satellites. Furthermore, compared with ambiguity-float solutions, the positioning accuracy of GPS AR + LEO AR solutions in east, north and up components is improved from 0.008, 0.008 and 0.027 m to 0.002, 0.003 and 0.011 m for 10-min sessions, respectively, and the fixing rate after time to first fix is almost 100%.

Description: With atmospheric corrections generated from the server, precise point positioning real-time kinematic (PPP–RTK) can achieve high-precision solutions in a fast convergence. PPP–RTK users are concerned about how to use the corrections and the level of performance that can be achieved; thus, our research has focused on correction methods, a priori stochastic modeling, and positioning performance evaluation. Conversely, it is crucial for the server to improve the precision of corrections provided and to consider the balance between cost, computation burden and user performance, especially for commercial applications. We use different scales of the national GPS network of France to generate ionospheric and tropospheric corrections, and corresponding uncertainty information is generated by establishing error functions with respect to an inter-station distance. The quality of corrections and corresponding user performance are analyzed with inter-station distances varying from 22 to 251 km. The results show that the precision of atmospheric corrections can be improved with an increasing number of stations in the network, but the improvement is not significant when the inter-station distances are smaller than 50 km. Regarding user performance, compared to conventional PPP solutions with ambiguity resolution, the convergence time can be reduced by a maximum of 93% and 85% in the horizontal and vertical components, respectively, when the inter-station distance is about 23 km. However, a station spacing within 100 km can still support a 3-min convergence; thus, a balance of server budget and user performance should be considered instead of a dense network.

Description: The Tibetan Plateau is a global hotspot of stratospheric intrusion, and elevated surface ozone was observed at ground monitoring sites. Still, links between the variability of surface ozone and stratospheric intrusion at the regional scale remain unclear. This study synthesized ground measurements of surface ozone over the Tibetan Plateau and analyzed their seasonal variations. The monthly mean surface ozone concentrations over the Tibetan Plateau peaked earlier in the south in April and May and later in the north in June and July. The migration of monthly surface ozone peaks was coupled with the synchronous movement of tropopause folds and the westerly jet that created conditions conducive to stratospheric ozone intrusion. Stratospheric ozone intrusion significantly contributed to surface ozone across the Tibetan Plateau, especially in the areas with high surface ozone concentrations during their peak-value month. We demonstrated that monthly variation of surface ozone over the Tibetan Plateau is mainly controlled by stratospheric intrusion, which warrants proper consideration in understanding the atmospheric chemistry and the impacts of ozone over this highland region and beyond.

Description: To understand the characteristics of particulate matter (PM) and other air pollutants in Xinjiang, a region with one of the largest sand-shifting deserts in the world and significant natural dust emissions, the concentrations of six air pollutants monitored in 16 cities were analyzed for the period January 2013–June 2019. The annual mean PM2.5, PM10, SO2, NO2, CO, and O3 concentrations ranged from 51.44 to 59.54 μg m−3, 128.43–155.28 μg m−3, 10.99–17.99 μg m−3, 26.27–31.71 μg m−3, 1.04–1.32 mg m−3, and 55.27–65.26 μg m−3, respectively. The highest PM concentrations were recorded in cities surrounding the Taklimakan Desert during the spring season and caused by higher amounts of wind-blown dust from the desert. Coarse PM (PM10-2.5) was predominant, particularly during the spring and summer seasons. The highest PM2.5/PM10 ratio was recorded in most cities during the winter months, indicating the influence of anthropogenic emissions in winters. The annual mean PM2.5 (PM10) concentrations in the study area exceeded the annual mean guidelines recommended by the World Health Organization (WHO) by a factor of ca. ∼5–6 (∼7–8). Very high ambient PM concentrations were recorded during March 19–22, 2019, that gradually influenced the air quality across four different cities, with daily mean PM2.5 (PM10) concentrations ∼8–54 (∼26–115) times higher than the WHO guidelines for daily mean concentrations, and the daily mean coarse PM concentration reaching 4.4 mg m−3. Such high PM2.5 and PM10 concentrations pose a significant risk to public health. These findings call for the formulation of various policies and action plans, including controlling the land degradation and desertification and reducing the concentrations of PM and other air pollutants in the region.