Hao Yang , Xiufeng He , Vagner Ferreira , Susu Song , Wei Zhan , Xinzhe Xu , Shengyue Ji
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Here we present a hierarchical framework incorporating two methods: ZTD-ER, combining a three-dimensional voxel empirical model (EZTD) with radial basis function interpolation, and ZTD-GR, integrating Global Forecast System (GFS) data with radial basis function interpolation. The respective average RMSE values are 6.85 mm and 5.90 mm. We found that this represents accuracy improvements of 46–67 % compared to traditional polynomial and spherical harmonic methods, while the EZTD-based approach maintains near-optimal performance (6.85 mm RMSE) even when GFS data is unavailable, demonstrating superior robustness across different elevations and seasons. Our results provide a reliable solution for high-precision real-time ZTD mapping in challenging regions where GNSS networks are sparse. 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引用次数: 0
摘要
天顶对流层延迟(ZTD)表示电磁波在中性大气中传播时的信号延迟,是卫星导航中的一个重要误差源,也是气象应用中的一个有价值的参数。全球导航卫星系统(GNSS)技术提供了高精度的ZTD估计,但稀疏的站点覆盖对实时ZTD制图构成了根本性的挑战,特别是在地形复杂的地区,传统的插值方法由于对ZTD垂直分层的描述不足,精度有限。本文提出了一种结合三维体素经验模型(EZTD)与径向基函数插值相结合的ZTD-ER和将全球预报系统(GFS)数据与径向基函数插值相结合的ZTD-GR两种方法的分层框架。平均RMSE值分别为6.85 mm和5.90 mm。我们发现,与传统的多项式和球谐方法相比,这代表了46 - 67%的精度提高,而基于eztd的方法即使在GFS数据不可用的情况下也能保持接近最佳的性能(6.85 mm RMSE),显示出在不同海拔和季节的优越鲁棒性。我们的研究结果为在GNSS网络稀疏的具有挑战性的地区进行高精度实时ZTD测绘提供了可靠的解决方案。该战略解决了当前对流层建模能力的关键限制,为实时GNSS气象监测提供了更高的空间分辨率,并为精密卫星导航应用提供了更好的大气校正。该框架的操作灵活性和已证明的准确性使其对世界各地稀疏站点地区的极端天气预警系统特别有价值。
Zenith tropospheric delay (ZTD) represents the signal retardation of electromagnetic waves propagating through the neutral atmosphere, serving as both a critical error source in satellite navigation and a valuable parameter for meteorological applications. Global Navigation Satellite System (GNSS) technology provides high-precision ZTD estimation, but sparse station coverage poses fundamental challenges for real-time ZTD mapping, particularly in regions with complex terrain where conventional interpolation methods exhibit limited accuracy due to inadequate characterization of ZTD’s vertical stratification. Here we present a hierarchical framework incorporating two methods: ZTD-ER, combining a three-dimensional voxel empirical model (EZTD) with radial basis function interpolation, and ZTD-GR, integrating Global Forecast System (GFS) data with radial basis function interpolation. The respective average RMSE values are 6.85 mm and 5.90 mm. We found that this represents accuracy improvements of 46–67 % compared to traditional polynomial and spherical harmonic methods, while the EZTD-based approach maintains near-optimal performance (6.85 mm RMSE) even when GFS data is unavailable, demonstrating superior robustness across different elevations and seasons. Our results provide a reliable solution for high-precision real-time ZTD mapping in challenging regions where GNSS networks are sparse. This strategy addresses critical limitations in current tropospheric modeling capabilities, offering enhanced spatial resolution for real-time GNSS meteorological monitoring and improved atmospheric corrections for precision satellite navigation applications. The framework’s operational flexibility and demonstrated accuracy make it particularly valuable for extreme weather early warning systems in sparse stations areas around the world.
期刊介绍:
Contributions are invited on novel achievements in all fields of measurement and instrumentation science and technology. Authors are encouraged to submit novel material, whose ultimate goal is an advancement in the state of the art of: measurement and metrology fundamentals, sensors, measurement instruments, measurement and estimation techniques, measurement data processing and fusion algorithms, evaluation procedures and methodologies for plants and industrial processes, performance analysis of systems, processes and algorithms, mathematical models for measurement-oriented purposes, distributed measurement systems in a connected world.