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Real-time GNSS tropospheric delay estimation with a novel global random walk processing noise model (GRM)

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Abstract

Accurate modeling of tropospheric delays is crucial for the global navigation satellite system (GNSS), which finds extensive applications in early warning systems of natural hazards and extreme weather forecasting. Zenith tropospheric delay (ZTD) is estimated as a random walk process with a constraint in GNSS processing. The constraint, referred to as random walk process noise (RWPN), holds significant importance in real-time ZTD estimation and exhibits geographical and temporal specificity. Presently, RWPN is treated as either a constant value or derived from a numerical weather model (NWM). To address this, our study presents a global RWPN model (GRM) by parameterizing a decade of NWM-derived RWPN data. Taking into account its spatiotemporal nature, we formulate the RWPN equation for each station by employing trigonometric, exponential, and Legendre functions. The optimum RWPN value is determined by incorporating GRM using latitude, longitude, orthometric height, and time as inputs. To validate the efficacy of GRM, we compare its performance against RWPN values derived from both JRA-55 and ERA5 datasets for the year 2020. The results indicate that the GRM-derived values exhibit enhanced accuracy in comparison with the optimal fixed RWPN values, as well as the yearly and monthly mean RWPN values. Additionally, we assess the efficacy of the GRM model in real-time ZTD estimation across 20 globally distributed GNSS stations. The results reveal an improvement exceeding 10% when compared to the results of the best fixed RWPN values. The GRM model offers an effective solution for obtaining accurate RWPN values on a global level, all while minimizing computational demands and time constraints. This notable progress significantly bolsters the precision of real-time GNSS estimates, thus facilitating their application in time-sensitive geophysical and meteorological scenarios.

Highlights

A global RWPN model is introduced for real-time GNSS tropospheric delay estimation. The model provides precise RWPN values while minimizing computation cost and time. The proposed model improves the accuracy of real-time ZTD estimation by over 10%. This model promotes GNSS in time-critical geophysical applications.

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Data Availability

We would like to thank Dr. Florian Zus for the ERA5 data-derived GFZ-VMF1 product (https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.bd0915c6? tab = overview), International GNSS Service (IGS) for providing the GNSS observations, and final tropospheric products (https://cddis.nasa.gov/archive/gnss/products/troposphere /zpd/), Centre National d’Études Spatiales (CNES) for providing real-time orbit and clock products (http://www.ppp-wizard.net/products/REAL_TIME/). The model proposed in this study and the output results are hosted within the Zenodo repository (https://zenodo.org/record/6967015#.Yu0IrnZBxD8).

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Acknowledgements

This research is financially supported by the National Natural Science Foundation of China (42204018, 41974029) and the National Key R&D Program of China (2021YFC3000504), the State Key Laboratory of Geodesy and Earth’s Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, China (Grant SKLGED2022-3-8). The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.

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Authors and Affiliations

  1. College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, People’s Republic of China

    Zhilu Wu

  2. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430072, People’s Republic of China

    Zhilu Wu, Cuixian Lu, Yuxuan Tan & Yuxin Zheng

  3. First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, People’s Republic of China

    Zhilu Wu, Yang Liu & Yanxiong Liu

  4. State Key Laboratory of Geodesy and Earth’s Dynamics, Wuhan, 430077, People’s Republic of China

    Zhilu Wu

  5. Chongqing Branch, Changjiang River Scientific Research Institute, Chongqing, 400026, People’s Republic of China

    Ke Jin

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  1. Zhilu Wu
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Contributions

ZW and CL proposed this study and developed the methodology. YT, YZ, and YL conducted the experiments for data collection and processing. ZW and CL wrote the manuscript. YL and KJ edited the manuscript. All authors were involved in discussions and provided critical feedback to the manuscript.

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Correspondence to Cuixian Lu.

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Wu, Z., Lu, C., Tan, Y. et al. Real-time GNSS tropospheric delay estimation with a novel global random walk processing noise model (GRM). J Geod 97, 112 (2023). https://doi.org/10.1007/s00190-023-01780-8

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