Assessing the Performance of GPS Precise Point Positioning Under Different Geomagnetic Storm Conditions during Solar Cycle 24

Ensemble Machine Learning of Random Forest, AdaBoost and XGBoost for Vertical Total Electron Content Forecasting

Space weather describes varying conditions between the Sun and Earth that can degrade Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be precisely and timely corrected for accurate and reliable GNSS applications. That can be modeled with the Vertical Total Electron Content (VTEC) in the Earth’s ionosphere. This study investigates different learning algorithms to approximate nonlinear space weather processes and forecast VTEC for 1 h and 24 h in the future for low-, mid- and high-latitude ionospheric grid points along the same longitude. VTEC models are developed using learning algorithms of Decision Tree and ensemble learning of Random Forest, Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost). Furthermore, ensemble models are combined into a single meta-model Voting Regressor. Models were trained, optimized, and validated with the time series cross-validation technique. Moreover, the relative importance of input variables to the VTEC forecast is estimated. The results show that the developed models perform well in both quiet and storm conditions, where multi-tree ensemble learning outperforms the single Decision Tree. In particular, the meta-estimator Voting Regressor provides mostly the lowest RMSE and the highest correlation coefficients as it averages predictions from different well-performing models. Furthermore, expanding the input dataset with time derivatives, moving averages, and daily differences, as well as modifying data, such as differencing, enhances the learning of space weather features, especially over a longer forecast horizon.

Ionospheric Behavior during the 10 June 2021 Annular Solar Eclipse and Its Impact on GNSS Precise Point Positioning

The main effects of the 10 June 2021 annular solar eclipse on GNSS position estimation accuracy are presented. The analysis is based on TEC measurements made by 2337 GNSS stations around the world. TEC perturbations were obtained by comparing results 2 days prior to and after the day of the event. For the analysis, global TEC maps were created using ordinary Kriging interpolation. From TEC changes, the apparent position variation was obtained using the post-processing kinematic precise point positioning with ambiguity resolution (PPP-AR) mode. We validated the TEC measurements by contrasting them with data from the Swarm-A satellite and four digiosondes in Central/South America. The TEC maps show a noticeable TEC depletion (<−60%) under the moon’s shadow. Important variations of TEC were also observed in both crests of the Equatorial Ionization Anomaly (EIA) region over the Caribbean and South America. The effects on GNSS precision were perceived not only close to the area of the eclipse but also as far as the west coast of South America (Chile) and North America (California). The number of stations with positioning errors of over 10 cm almost doubled during the event in these regions. The effects were sustained longer (∼10 h) than usually assumed.

Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision

In this work, we present the positioning error analysis of the 12 May 2021 moderate geomagnetic storm. The storm happened during spring in the northern hemisphere (fall in the south). We selected 868 GNSS stations around the globe to study the ionospheric and the apparent position variations. We compared the day of the storm with the three previous days. The analysis shows the global impact of the storm. In the quiet days, 93% of the stations had 3D errors less than 10 cm, while during the storm, only 41% kept this level of accuracy. The higher impact was over the Up component. Although the stations have algorithms to correct ionospheric disturbances, the inaccuracies lasted for nine hours. The most severe effects on the positioning errors were noticed in the South American sector. More than 60% of the perturbed stations were located in this region. We also studied the effects produced by two other similar geomagnetic storms that occurred on 27 March 2017 and on 5 August 2019. The comparison of the storms shows that the effects on position inaccuracies are not directly deductible neither from the characteristics of geomagnetic storms nor from enhancement and/or variations of the ionospheric plasma.

Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver

Nowadays, with the incursion of low-cost GNSS receivers with modern characteristics, it is common to investigate and apply new methodologies and solutions with different receivers of this nature. Based on this fact, the performance of the solution obtained from the low-cost GNSS receiver is evaluated compared to a geodetic grade GNSS receiver at different sampling frequencies for the PPP-static and PPP-kinematic modes. For this, the original RINEX observation files were analyzed and decimated into different sampling rates as 0.1, 0.2, 1, 5, 15 and 30 s with TEQC software. All RINEX files were submitted to the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) online service for processing with static and kinematic modes. The PPP-derived coordinates from the low-cost GNSS receiver were compared with the geodetic receiver to evaluate the obtained solution. The results reveal that the behavior of all studied sampling rates from the low-cost GNSS receiver are constant in achieved positioning. In addition, the achieved precision shows that it is recommendable to use a high sampling rate to obtain a cm level in PPP-static mode by using a low-cost GNSS receiver, this mode being the most accurate and potential alternative for structural health monitoring studies, mapping and positioning in urban areas.

Degradation of Kinematic PPP of GNSS Stations in Central Europe Caused by Medium-Scale Traveling Ionospheric Disturbances During the St. Patrick’s Day 2015 Geomagnetic Storm

In solar cycle 24, the strongest geomagnetic storm took place on 17 March 2015, when the geomagnetic activity index was as high as −223 nT. To verify the impact that the storm had on the Global Navigation Satellite System (GNSS)’s positioning accuracy and precision, we used 30-s observations from 15 reference stations located in Central Europe. For each of them, we applied kinematic precise point positioning (PPP) using gLAB software for the day of the storm and, for comparison, for a selected quiet day (13 March 2015). Based on the conducted analyses, we found out that the position root mean square (RMS) values on the day of the geomagnetic storm were significantly high and amounted to several dozen centimeters. The average RMS for the altitude coordinates was 0.58 m between 12:00 and 24:00 (GPS time), and 0.37 and 0.26 m for directions North and East, respectively. The compromised accuracy level was caused by a sudden decrease in the number of satellites used for calculations. This was due to a high number of cycle slips (CSs) detected during this period. The occurrence of these effects was strictly correlated with the appearance of traveling ionospheric disturbances (TIDs). This was proven by analyzing changes in the total electron content (TEC) estimated for each station–satellite pair.

Small-Scale Ionospheric Irregularities of Auroral Origin at Mid-latitudes during the 22 June 2015 Magnetic Storm and Their Effect on GPS Positioning

Small-scale ionospheric irregularities affect navigation and radio telecommunications. We studied small-scale irregularities observed during the 22 June 2015 geomagnetic storm and used experimental facilities at the Institute of Solar-Terrestrial Physics of the Siberian Branch of the Russian Academy of Sciences (ISTP SB RAS) located near Irkutsk, Russia (~52°N, 104°E). The facilities used were the DPS-4 ionosonde (spread-F width), receivers of the Irkutsk Incoherent Scatter Radar (Cygnus A signal amplitude scintillations), and GPS/GLONASS receivers (amplitude and phase scintillations), while 150 MHz Cygnus A signal recording provides a unique data set on ionosphere small-scale structure. We observed increased spread-F, Cygnus A signal amplitude scintillations, and GPS phase scintillations near 20 UT on 22 June 2015 at mid-latitudes. GPS/GLONASS amplitude scintillations were at a quiet time level. By using global total electron content (TEC) maps, we conclude that small-scale irregularities are most likely caused by the auroral oval expansion. In the small-scale irregularity region, we recorded an increase in the precise point positioning (PPP) error. Even at mid-latitudes, the mean PPP error is at least five times that of the quiet level and reaches 0.5 m.

Precise Point Positioning on the Reliable Detection of Tropospheric Model Errors

Precise point positioning (PPP) is one of the well-known applications of Global Navigation Satellite System (GNSS) and provides precise positioning solutions using accurate satellite orbit and clock products. The tropospheric delay due to the neutral atmosphere for microwave signals is one of the main sources of measurement error in PPP. As one component of this delay, the hydrostatic delay is usually compensated by using an empirical correction model. However, how to eliminate the effects of the wet delay during a weather event is a challenge because current troposphere models are not capable of considering the complex atmosphere around the receiver during situations such as typhoons, storms, heavy rainfall, et cetera. Thus, how positioning results can be improved if the residual wet delays are taken into account needs to be investigated . In this contribution, a real-time procedure of recursive detection, identification and adaptation (DIA) is applied to detect the model errors which have the same effects on both phase and code observables; e.g., the model error caused by the tropospheric delay. Once the model errors are identified, additional parameters are added to the functional model to account for the measurement residuals. This approach is evaluated with Global Positioning System (GPS) data during two rainfall events in Darwin, Australia, proving the usefulness of compensated residual slant wet delay for positioning results. Comparisons with the standard approach show that the precision of the up component is improved significantly during the periods of the weather events; for the two case studies, 72.46% and 64.41% improvements of root mean squared error (RMS) resulted, and the precision of the horizontal component obtained by the proposed approach is also improved more than 30% compared to the standard approach. The results also show that the identified model errors are concentrated at the beginning of both heavy rainfall processes when the front causes significant spatial and temporal gradients of the integrated water vapor above the receiver.