Variability of the Tropospheric-Delay Temporal Structure Function of Radio Signals from the Global Navigation Satellite Systems Versus Tropospheric Surface Layer Parameters

Khutorov, V. E.; Teptin, G. M.; Zhuravlev, A.A.; Khutorova, O. G.

doi:10.1007/s11141-016-9704-8

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…The hot and wet conditions are closely related to the tropospheric effect, especially the wet components, which are approximately proportional to the content of water vapor in the troposphere [17]. Moreover, a strong seasonal and interannual variability of mesoscale fluctuations of the zenith tropospheric delay of decimeter radio waves was detected in [31]. For further study, the influence of temperature and humidity on the accuracy of the ZTD and the variation curve of the time series of the ZTD residual are discussed.…”

Section: Analysis Of Influencing Factors Of Ztd Residuals Inverted By Nwp Modelmentioning

confidence: 97%

“…The possible reasons for this would be: (1) the temperature in low-latitude regions is usually higher than that in high-latitude regions. The higher the temperature, the more violent the atmosphere and the harder it is to calculate the ZTD using meteorological data [30,31]. Therefore, high temperature may reduce the accuracy of the ZTD inverted by NWP.…”

Section: Analysis Of Influencing Factors Of Ztd Residuals Inverted By Nwp Modelmentioning

confidence: 99%

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A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model

Yan

et al. 2020

Sensors

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Tropospheric delay is a major error source that affects the initialization and re-initialization speed of the Global Navigation Satellite System’s (GNSS) medium-/long-range baseline in Network Real-Time Kinematic (NRTK) positioning. Fusing the meteorological data from the Numerical Weather Prediction (NWP) model to estimate the zenith tropospheric delay (ZTD) is one of the current research hotspots. However, research has shown that the ZTD derived from NWP models is still not accurate enough for high-precision GNSS positioning applications without the estimation of the residual tropospheric delay. To date, General Regression Neural Network (GRNN) has been applied in many fields. It has a high learning speed and simple structure, and can approximate any function with arbitrary precision. In this study, we developed a regional NWP tropospheric delay inversion method based on a GRNN model to improve the accuracy of the tropospheric delay derived from the NWP model. The accuracy of the tropospheric delays derived from reanalysis data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the US National Centers for Environmental Prediction (NCEP) was assessed through comparisons with the results of the International GPS Service (IGS). The variation characteristics of the residual of the ZTD inverted by NWP data were analyzed considering the factors of temperature, humidity, latitude, and season. To evaluate the performance of this new method, the National Center Atmospheric Research (NCAR) troposphere data of 650 stations in Japan in 2005 were collected as a reference to compare the accuracy of the ZTD before and after using the new method. The experimental results showed that the GRNN model has obvious advantages in fitting the NWP ZTD residual. The mean residual and the root mean square deviation (RMSD) of the ZTD inverted using the method of this study were 9.5 mm and 12.7 mm, respectively, showing reductions of 20.8% and 19.1%, respectively, as compared to the standard NWP model. For long-range baseline (155 km and 207 km), the corrected NWP-constrained RTK showed a reduction of over 43% in the initialization time compared with the standard RTK, and showed a reduction of over 24% in the initialization time compared with the standard NWP-constrained RTK.

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Section: Analysis Of Influencing Factors Of Ztd Residuals Inverted By Nwp Modelmentioning

confidence: 97%

Section: Analysis Of Influencing Factors Of Ztd Residuals Inverted By Nwp Modelmentioning

confidence: 99%

A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model

Yan

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

Variability of the Tropospheric-Delay Temporal Structure Function of Radio Signals from the Global Navigation Satellite Systems Versus Tropospheric Surface Layer Parameters

Cited by 1 publication

References 21 publications

A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model

A Regional NWP Tropospheric Delay Inversion Method Based on a General Regression Neural Network Model

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