Study Shows How LEO Satellites Improve GNSS Severe Weather Position Monitoring

 

RMS of horizontal coordinate (top), vertical coordinate (middle), and ZTD (bottom) derived from GNSS PPP solutions in the severe environment from 3 to 9 March 2022.

Revolutionary GNSS Study Shows How LEO Satellites Improve Severe Weather Monitoring

Researchers have unveiled groundbreaking advancements in global navigation satellite system (GNSS) data processing, thanks to innovative tropospheric modeling techniques and the inclusion of low Earth orbit (LEO) satellites. The study, published in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, demonstrates how these developments significantly enhance accuracy in severe weather monitoring and positioning applications.

Led by Xinyu Zhang and colleagues from the Chinese Academy of Sciences, the research evaluated several tropospheric horizontal gradient models under stable and severe weather conditions. Results show that second-order gradient models offer superior accuracy, achieving precision of 1.1 mm horizontally and 0.8 mm in zenith total delay (ZTD) during stable conditions, and maintaining impressive performance during extreme weather.

Critically, the addition of LEO satellites was found to improve spatial and temporal resolution, with accuracy gains of up to 34.9% for ZTD measurements during severe weather. These improvements promise transformative impacts on weather forecasting, GNSS-based applications, and climate research.

“This work highlights the power of combining advanced models with LEO constellations,” said Dr. Wenwu Ding, co-author and precision measurement expert. “These methods offer not only better accuracy but also resilience in extreme conditions, paving the way for a new era in GNSS-based meteorology.”

While traditional directional gradient models showed limitations in performance, particularly at high elevations, this research underscores the importance of high-temporal-resolution gradient estimation to capture rapid changes in weather. The findings advocate for adopting advanced models and leveraging LEO technology for more reliable and precise GNSS data.

With applications ranging from disaster response to aviation safety, the study marks a significant leap forward in the quest for more accurate and robust weather monitoring systems. Researchers hope the technology will soon be integrated into operational GNSS frameworks worldwide.

Summary

The paper, "Comparison of Tropospheric Horizontal Gradients Modeling Methods in LEO Constellation Augmented GNSS Precise Point Positioning," evaluates various tropospheric gradient models to improve GNSS data accuracy, particularly under severe weather conditions. It highlights:

1. Model Comparisons: High-order gradient models, such as the second-order model, outperform traditional tilted-plane models in modeling asymmetric tropospheric delays, particularly in severe weather.
2. LEO Augmentation: Adding low Earth orbit (LEO) constellations improves spatial and temporal resolutions, enhancing the accuracy of horizontal components, vertical components, and zenith total delay (ZTD).
3. Results: During stable and severe periods, the second-order gradient model achieves accuracies of up to 1.1/3.8/0.8 mm and 0.9/2.5/1.0 mm, respectively, for the horizontal, vertical components, and ZTD.
4. Limitations of Directional Models: Directional gradient models struggle with high-elevation data and show lower performance due to parameter redundancy and correlation issues.
5. Temporal Resolution Importance: High-temporal-resolution gradient parameters significantly improve ZTD and SPD accuracy, critical for severe weather monitoring.

The study emphasizes adopting advanced models and leveraging LEO constellations for enhanced GNSS applications. Let me know if you need a deeper breakdown of any section.

Reference

Comparison of Tropospheric Horizontal Gradients Modeling Methods in LEO Constellation Augmented GNSS Precise Point Positioning

Xinyu Zhang, Wenwu Ding, Xiaochuan Qu, Hongjin Xu, Xuanzhao Tan, Yunbin Yuan

https://doi.org/10.1109/JSTARS.2024.3523023 

Abstract With the improvement in GNSS data processing accuracies, the selection of optimal asymmetric troposphere delay modeling method becomes essential, especially during the period of severe weather events and with the development of low Earth orbit (LEO) constellation augmented GNSS (LeGNSS). In this research, we compare the performances of several troposphere gradient models in describing the asymmetrical troposphere delays. Using simulation data during the stable and severe periods, we find that the high-order horizontal gradient models exhibit higher accuracy in the experiments. In the LeGNSS precision point positioning solutions, the second-order gradient model performs optimally, with accuracies of up to 1.1/3.8/0.8 mm during the stable period and 0.9/2.5/1.0 mm during the severe period for the horizontal component, vertical component, and zenith total delay (ZTD) parameters. In comparison, the analysis of slant path delays accuracy for elevation below 10°shows that the directional model is more suitable for low elevation observations, but the introduction of too many redundant parameters leads to a decrease in the accuracy at high elevation angles. The LEO constellation can bring maximum 32.9%, 12.6%, and 27.9% accuracy improvement for the horizontal component, vertical component, and ZTD parameters during the stable period, while 26.5%, 31.8%, and 34.9% during the severe period. The estimation of high-temporal-resolution gradient parameters instead of traditional daily gradient parameters can significantly improve the accuracy of ZTD in the extreme weather events. Therefore, this research underscores the spatial and temporal resolution of horizontal gradient models, which meets the growing demand for GNSS/LeGNSS data processing during the severe weather events.

Background of the study:
This study focuses on comparing different methods for modeling the asymmetrical tropospheric delays in GNSS (Global Navigation Satellite System) data processing. The troposphere can cause delays in GNSS signals, and these delays can be different in different directions around the GNSS receiver. Accurately modeling these asymmetrical tropospheric delays is important for precise GNSS positioning.

Research objectives and hypotheses:
The main objectives of this study are to evaluate the performance of various tropospheric horizontal gradient models, including the Fourier series expansion model and the directional model, and to investigate the potential contribution of low Earth orbit (LEO) constellation augmented GNSS (LeGNSS) in improving the modeling of these asymmetrical tropospheric delays.

Methodology:
The researchers simulate GNSS and LeGNSS observations under different tropospheric conditions, including a stable period and a severe weather period with heavy precipitation. They then compare the performance of different tropospheric gradient models, including the tilted plane, second-order, third-order, and directional models with 4, 6, and 8 gradient parameters, in modeling the simulated slant path delays. The researchers also conduct GNSS and LeGNSS precise point positioning (PPP) to evaluate the impact of these tropospheric gradient models on positioning and zenith total delay (ZTD) estimation.

Results and findings:
The results show that the high-order gradient models, particularly the second-order and third-order Fourier series expansion models, perform better than the tilted plane model in modeling the asymmetrical tropospheric delays, especially during the severe weather period. The directional model with 8 gradient parameters also performs well, but it tends to have higher correlations between the estimated parameters, leading to lower accuracies in the vertical component and ZTD estimation. The addition of LeGNSS observations can improve the positioning and ZTD estimation accuracies, with maximum improvements of 32.9%, 12.6%, and 27.9% for the horizontal, vertical, and ZTD components, respectively, during the stable period, and 26.5%, 31.8%, and 34.9% during the severe period.

Discussion and interpretation:
The researchers suggest that the second-order gradient model is the optimal choice for tropospheric delay modeling, as it provides the best balance between modeling accuracy and parameter estimation stability. The directional model, although performing well in modeling the low-elevation slant path delays, tends to introduce too many redundant parameters, leading to higher correlations between the estimated parameters and reduced accuracy in the vertical component and ZTD estimation.

Contributions to the field:
This study provides a comprehensive comparison of different tropospheric gradient modeling methods, including the Fourier series expansion and the directional models, and their impact on GNSS/LeGNSS positioning and ZTD estimation. It also highlights the potential of LeGNSS in improving the modeling of asymmetrical tropospheric delays, especially during severe weather events.

Achievements and significance:
The findings of this study suggest that the accurate modeling of tropospheric asymmetry is essential for precise GNSS/LeGNSS positioning and ZTD estimation, particularly in severe weather conditions. The second-order gradient model is identified as the optimal choice, and the addition of LeGNSS observations can further improve the accuracy of the results.

Limitations and future work:
The researchers note that the performance of the directional model in modeling the slant path delays at medium and high elevations requires further investigation. Additionally, the evaluation of new tropospheric gradient models, such as the B-spline mapping function, and the assessment of the methods using real GNSS/LeGNSS data in the future are necessary to validate the findings of this simulation-based study.