Abstract
Abstract. An analysis of processing settings impacts on estimated tropospheric gradients is presented. The study is based on the benchmark data set collected within the COST GNSS4SWEC action with observations from 430 Global Navigation Satellite Systems (GNSS) reference stations in central Europe for May and June 2013. Tropospheric gradients were estimated in eight different variants of GNSS data processing using precise point positioning (PPP) with the G-Nut/Tefnut software. The impacts of the gradient mapping function, elevation cut-off angle, GNSS constellation, observation elevation-dependent weighting and real-time versus post-processing mode were assessed by comparing the variants by each to other and by evaluating them with respect to tropospheric gradients derived from two numerical weather models (NWMs). Tropospheric gradients estimated in post-processing GNSS solutions using final products were in good agreement with NWM outputs. The quality of high-resolution gradients estimated in (near-)real-time PPP analysis still remains a challenging task due to the quality of the real-time orbit and clock corrections. Comparisons of GNSS and NWM gradients suggest the 3∘ elevation angle cut-off and GPS+GLONASS constellation for obtaining optimal gradient estimates provided precise models for antenna-phase centre offsets and variations, and tropospheric mapping functions are applied for low-elevation observations. Finally, systematic errors can affect the gradient components solely due to the use of different gradient mapping functions, and still depending on observation elevation-dependent weighting. A latitudinal tilting of the troposphere in a global scale causes a systematic difference of up to 0.3 mm in the north-gradient component, while large local gradients, usually pointing in a direction of increasing humidity, can cause differences of up to 1.0 mm (or even more in extreme cases) in any component depending on the actual direction of the gradient. Although the Bar-Sever gradient mapping function provided slightly better results in some aspects, it is not possible to give any strong recommendation on the gradient mapping function selection.
Highlights
When processing data from Global Navigation Satellite Systems (GNSS), a total signal delay due to the troposphere is modelled by epoch- and station-wise zenith total delay (ZTD) parameters, and, optimally, together with tropospheric gradients representing the first-order asymmetry of the total delay
Tropospheric gradients are not assimilated into numerical weather models (NWMs); they could be assimilated in future and they are essential for reconstructing slant total delays (STDs)
The mean differences stayed below 0.2 mm for ZTD and ±0.02 mm for tropospheric gradients with one exception for the latter parameter. This was a comparison between results provided by Chen and Herring (CH) and BS mfg where the mean differences reached −0.05 and 0.03 mm for the north- and east-gradient components, respectively. These small systematic effects can be attributed to the average difference between tropospheric gradients computed with BS mfg compared to CH mfg
Summary
When processing data from Global Navigation Satellite Systems (GNSS), a total signal delay due to the troposphere is modelled by epoch- and station-wise zenith total delay (ZTD) parameters, and, optimally, together with tropospheric gradients representing the first-order asymmetry of the total delay. Few attempts were made to compare the tropospheric gradients with independent estimates, i.e. those derived from water vapour radiometer (WVR) or NWM data. Li et al (2015) found an improvement of about %–35 % for the multi-GNSS processing when compared with NWMs and %–28 % when compared to WVR Another multi-GNSS study on tropospheric gradients (Zhou et al, 2017) used data from a global network of 134 GNSS stations processed in six different constellation combinations in July 2016. ZTDs and tropospheric gradients are compared with the ones estimated from two NWMs – ERA5, which is a global atmospheric reanalysis, and a limited-area short-range forecast utilizing the Weather Research and Forecasting (WRF) model. We quantified systematic differences in tropospheric gradients coming from the gradient mapping function and the method of observation weighting during a local event with strong wet gradients
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