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A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model
Lei Li1, Ying Xu1,2, Lizi Yan1
1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|June 7, 2020
Summary
This study improves Global Navigation Satellite System (GNSS) positioning accuracy by using a General Regression Neural Network (GRNN) model to refine tropospheric delay estimates from weather models. This enhances network real-time kinematic (NRTK) initialization speed and precision.
Area of Science:
- Geodesy and Geomatics Engineering
- Atmospheric Science and Meteorology
- Artificial Intelligence in Earth Sciences
Background:
- Tropospheric delay significantly impacts Global Navigation Satellite System (GNSS) accuracy, particularly for network real-time kinematic (NRTK) positioning initialization.
- Numerical Weather Prediction (NWP) models estimate zenith tropospheric delay (ZTD), but their accuracy is insufficient for high-precision GNSS without residual error correction.
- General Regression Neural Network (GRNN) offers high learning speed and function approximation capabilities, making it suitable for complex modeling tasks.
Purpose of the Study:
- To develop and evaluate a regional tropospheric delay inversion method using GRNN to enhance NWP model ZTD accuracy.
- To analyze the factors influencing ZTD residuals derived from NWP data.
- To assess the impact of the improved ZTD estimates on NRTK positioning performance.
Main Methods:
- Developed a GRNN-based model for regional tropospheric delay inversion, utilizing meteorological data.
- Assessed NWP-derived ZTD accuracy against International GPS Service (IGS) data from ECMWF and NCEP.
- Analyzed ZTD residuals based on temperature, humidity, latitude, and season.
- Validated the GRNN method using National Center Atmospheric Research (NCAR) troposphere data from 650 Japanese stations.
Main Results:
- The GRNN model effectively fitted NWP ZTD residuals, reducing mean residual by 20.8% and RMSD by 19.1% compared to standard NWP models.
- The improved ZTD estimates achieved mean residual of 9.5 mm and RMSD of 12.7 mm.
- Corrected NWP-constrained RTK demonstrated over 43% reduction in initialization time for long-range baselines compared to standard RTK.
- A reduction of over 24% in initialization time was observed compared to standard NWP-constrained RTK.
Conclusions:
- The GRNN-based method significantly improves the accuracy of tropospheric delay estimation from NWP models.
- Enhanced ZTD accuracy leads to substantial improvements in NRTK positioning, particularly in reducing initialization times for medium- to long-range baselines.
- This approach offers a viable solution for high-precision GNSS applications requiring accurate real-time tropospheric delay modeling.
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