Abstract
Tropospheric delay is one of the main errors in precise point positioning (PPP). The inaccuracy of the tropospheric delay model will inevitably lead to a decrease in PPP accuracy. Therefore, the influence of the tropospheric gradient on the positioning accuracy should be considered in the processing of tropospheric delay. At the same time, the effects of different mapping function models and meteorological parameter calculation methods on the tropospheric delay accuracy of single-frequency PPP (SF PPP) are analyzed. Twelve MGEX stations, which are evenly distributed in the world, are used in this article. Taking into account the seasonal variation of the tropospheric delay, the observation times adopted are 2016, 2017, and 2018 for different seasons (winter, day of year (DOY): 22–28; spring, DOY: 92–98; summer, DOY: 199–205; and autumn, DOY: 275–281). Then, according to different mapping function models and meteorological parameter calculation methods, a total of 7056 tests and 9072 tests are performed, respectively. The following results were obtained after comparative analysis. (1) When the same method is used for calculating meteorological parameters, the percentage with improved tropospheric delay repeatability calculated by the Hopfield mapping function model (MFM3) is the highest, reaching more than 70%, and by Vienna Mapping Functions 3 (VMF3, grid resolution is 1°), the mapping function model (MFM8) is the lowest, less than 67.5%. The percentage with improved position repeatability is highest in the north (N) direction and lowest in the up (U) direction. (2) Using the same mapping function model, the correction of the tropospheric gradient model has a greater impact on calculating the repeatability percentage of the tropospheric delay and the position. Compared with standard atmospheric parameters, other calculation methods of meteorological parameters have little effect on the percentage increase of the tropospheric delay value and the positioning result after adding the tropospheric gradient model. It shows that different calculation methods of meteorological parameters have little effect on the calculation of tropospheric delay and position, different mapping function models have a large effect on the calculation of tropospheric delay and position, and the tropospheric gradient model has the greatest influence on the calculation of tropospheric delay and position.
Highlights
Experiment and AnalysisIn order to verify the influence of mapping function and meteorological parameter calculation method on the tropospheric delay, the tropospheric delay product published by IGS was taken as the reference true value
Tropospheric delay is one of the main errors in precise point positioning (PPP). e inaccuracy of the tropospheric delay model will inevitably lead to a decrease in PPP accuracy. erefore, the influence of the tropospheric gradient on the positioning accuracy should be considered in the processing of tropospheric delay
(1) When the same method is used for calculating meteorological parameters, the percentage with improved tropospheric delay repeatability calculated by the Hopfield mapping function model (MFM3) is the highest, reaching more than 70%, and by Vienna Mapping Functions 3 (VMF3, grid resolution is 1°), the mapping function model (MFM8) is the lowest, less than 67.5%. e percentage with improved position repeatability is highest in the north (N) direction and lowest in the up (U) direction
Summary
In order to verify the influence of mapping function and meteorological parameter calculation method on the tropospheric delay, the tropospheric delay product published by IGS was taken as the reference true value. Taking into account the seasonality of the tropospheric delay, 2016, 2017, and 2018 data for different seasons (winter, day of year (DOY): 22–28; spring, DOY: 92–98; summer, DOY: 199–205; and autumn, DOY: 275–281) [24] were used to evaluate the tropospheric delay and position of different models. Θ θ, dmjd, φ, h, λ and ERA-40 data at 15∘ × 15∘grid P, T, θ θ, DOY, φ, h θ, dmjd, h, φ, ah, aw,VMF1 site file θ, dmjd, h, φ, λ,VMF1 gridded file, orography_ell file θ, dmjd, h, φ, λ, ah, aw, VMF3 site file θ, dmjd, h, φ, λ, VMF3 gridded file, orography_ell file mfh, mfw mfh, mfw mfh, mfw mfh, mfw mfh, mfw mfh, mfw, zhd, zwd mfh, mfw mfh, mfw, zhd, zwd φ and λ represent the station latitude and longitude, h represents the altitude of station, DOY represents the day of year, θ is the elevation angle, P is the surface pressure, T is the surface temperature, dmjd denotes modified Julian data, ERA-40 is a European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis of the global atmosphere and surface conditions over the period 1957–2002, ah and aw are coefficients for determining Vienna Mapping Functions (VMF1), mfh and mfw are hydrostatic and wet mapping functions, respectively, and zhd and zwd are zenith hydrostatic delay and zenith wet delay, respectively
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