Real‐Time Monitoring of Ionosphere VTEC Using Multi‐GNSS Carrier‐Phase Observations and B‐Splines

Erdogan, Eren; Schmidt, Michaël; Goss, Andreas; Görres, Barbara; Seitz, Florian

doi:10.1029/2021sw002858

Cited by 9 publications

(3 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, considering that the final GIMs are usually provided with a time delay of 1-2 weeks, and the rapid GIMs with 1-2 days [53], their application in real-time is not possible. There are also ultra-rapid GIMs provided with latency of 2-3 h [54] and real-time (RT) GIMs [53,55]. The accuracy of RT GIMs is typically worse than final, post-processed GIMs due to the shorter span of observations, higher noise in carrier-to-code leveling, and difficulty in carrier ambiguity estimation in real-time processing mode [53].…”

mentioning

confidence: 99%

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

2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

mentioning

confidence: 99%

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

2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The JASON-3 altimetry satellite with a mean orbital altitude of approximately 1336 km can directly obtain the JASON-3 VTEC observations using the dual-frequency signals, i.e., Ku-band (13.575 GHz) and C-band (5.3 GHz) [26,27]. The calculation formula of the JASON-3 VTEC is expressed as Equation ( 3) below [28,29]. The sampling frequency of the JASON-3 satellite is 1 Hz [12].…”

Section: Validation Against Jason Vtecmentioning

confidence: 99%

Investigating the Performance of IGS Real-Time Global Ionospheric Maps under Different Solar Conditions

Liu,

Ren,

2023

Remote Sensing

View full text Add to dashboard Cite

In recent years, real-time global ionospheric map (RT-GIM) products have been actively developed by the international global navigation satellite system (GNSS) service (IGS) and its ionosphere associate analysis centers (IAACs) along with the increase of RT-GNSS multi-frequency and multi-constellation observations. In this study, the accuracy and consistency of three RT-GIM products from the Chinese Academy of Sciences (CAS), Wuhan University (WHU), and IGS are evaluated and analyzed utilizing three validation methods, namely, comparison with JASON-3 vertical total electron content (VTEC), the difference of slant total electron content (dSTEC), and IGS combined final GIM (IGSG) data. The test period was from 1 January 2019 to 31 December 2022, including the different solar activities. First, the comparison with JASON-3 data illustrates that the quality of the three RT-GIM products over oceans is in great consistency with that of the IGSG during different levels of solar activity and the daily mean bias (MEAN) values in low and high solar activities are approximately 5 and 10 TECU, respectively. The root mean square (RMS) values under low and high solar activities can be up to 7 and 12 TECU. Furthermore, the dSTEC validation results present that the MEAN values of RT-GIM products from different IAACs at high- and mid-latitude stations are about 0.5 TECU, which is smaller than those at low-latitude stations at about 1 TECU over continental regions. The standard deviation (STD) and RMS values for various RT-GIM products are within 3 and 4 TECU at low latitudes, respectively. In terms of the comparison with IGSG, the result shows that IGS combined RT-GIM (IRTG) presents better consistency than CAS RT-GIM (CRTG) and WHU RT-GIM (WRTG) in 2021 and 2022, with average annual STD and RMS values of 2.56 and 2.78 TECU, respectively. The daily biases of the RT-GIM products relative to IGSG can reach 4 TECU in high solar activities and the daily STD and RMS values are mainly within the 5 to 6 TECU range, respectively.

show abstract

“…Thus, if one derives thermospheric variations only from TLEs, what obtained is only the integral changes with temporal resolution around 1 day along the specific orbits. However, if the number of spacecraft (and even space debris) is taken into count, and each orbital object is considered as an orbiting station to the thermosphere, the integrated usage of these stations' observations could bring new information of the thermosphere, just like the GNSS signals are used to describe the ionosphere (Erdogan et al., 2021; Jakowski et al., 2012; Mannucci et al., 1998; Ren et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

An Index Description of the General Characteristics of Thermospheric Density Based on the Two‐Line‐Element Data Sets and the Spectral Whitening Method

Wu,

Mao,

Wang

et al. 2024

JGR Space Physics

View full text Add to dashboard Cite

The thermospheric density and its variations are crucial to aerospace activities as well as space weather research and operation. However, due to the difficulties in observing the thermosphere, there has been a lack of effective descriptions for the general characteristics of thermospheric density. In this paper, the Two‐Line‐Element data sets (TLEs) from multi‐target low Earth orbit satellites are used to derive a proxy of the daily average atmospheric density in the thermospheric shell located in the vicinity of LEOs' orbital altitude. It captures the overall characteristics of the thermosphere and exhibits good correlations (∼0.9) with modeled and observed thermospheric density. By applying the spectral whitening method to this proxy, a new index is derived to describe non‐periodic perturbation of the density where the specific satellite passed by. The fact that the obtained from different satellites within the same thermospheric shell presents significant consistency to each other means that the new index is a good indicator for the overall feature of the variations of thermospheric density, and it is possible to define a unified regional index to describe density disturbances for the thermospheric shell where these satellites fly through. Moreover, the at different altitudes also present good consistency suggesting the possibility of defining a global index , capable of describing the density variation of the entire thermosphere.

show abstract

Real‐Time Monitoring of Ionosphere VTEC Using Multi‐GNSS Carrier‐Phase Observations and B‐Splines

Cited by 9 publications

References 59 publications

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

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

Investigating the Performance of IGS Real-Time Global Ionospheric Maps under Different Solar Conditions

An Index Description of the General Characteristics of Thermospheric Density Based on the Two‐Line‐Element Data Sets and the Spectral Whitening Method

Contact Info

Product

Resources

About