Evaluation of the Possibility of Using the Predicted Tropospheric Delays in Real Time Gnss Positioning

Abstract

Among many sources of errors that influence Global Navigation Satellite System (GNSS) observations, tropospheric delay is one of the most significant. It causes nonrefractive systematic bias in the observations on the level of several meters, depending on the atmospheric conditions. Tropospheric delay modelling plays an important role in precise positioning. The current models use numerical weather data for precise estimation of the parameters that are provided as a part of the Global Geodetic Observation System (GGOS). The purpose of this paper is to analyze the tropospheric data provided by the GGOS Atmosphere Service conducted by the Vienna University of Technology. There are predicted and final delay data available at the Service. In real time tasks, only the predicted values can be used. Thus it is very useful to study accuracy of the forecast delays. Comparison of data sets based on predicted and real weather models allows for conclusions concerning possibility of using the former for real time positioning applications. The predicted values of the dry tropospheric delay component, both zenith and mapped, can be safely used in real time PPP applications, but on the other hand, while using the wet predicted values, one should be very careful.

Time series analysis of radio signal WET tropospheric delays for short-term forecast

“…In case of LAMA station, the forecast is not so accurate (differences from -33 mm to 35 mm), but still comparable with differences determined between the forecast and final zw T from Vienna service (Kalita et al, 2014). Respective mean values are -1.5 mm and 0.3 mm, standard deviations are 15 and 18 mm.…”

“…The product is available for the IGS sites as well as in the GRID form, enabling its global application (Böhm et al, 2008). The analysis of the forecast zenith delays and mapping parameter for both dry and wet components with regards to the final VMF1 parameters was already performed by Kalita et al (2014), who reported the 1.2 cm standard deviation of the final VMF zenith wet delay. In addition, this study shows that the parameter quality was positively correlated with the amplitude of the delay and the length of the prediction.…”

“…Since the wet tropospheric delay a priori value precise prediction is currently unachievable, in most of the applications its residual is estimated as an additional parameter. In models (Kalita et al, 2014). In addition, since the VMF1 forecast is based on the external weather data, the delay in providing those may lead to temporal unavailability of the required product.…”

GPS/GLONASS precise point positioning with IGS Real-time service product

“…Between 22nd May and 5th June 2014 (142-156 days of year (DOY) 2014) we registered three streams of corrections: IGS01, IGS02 and IGS03. We then used these corrections to post-process the satellite observations from five randomly chosen IGS stations located at or by entering into model surface meteorological data from global or regional numerical meteorological models such as European Centre for Medium-Range Weather Forecast (ECMWF), the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) or Global Pressure and Temperature (GPT) (Boehm et al, 2007;Kalita et al, 2014). In this article, we will describe only the Saastamoinen model, because it is used in the processing of observation using the BKG program.…”

GNSS-Warp Software for Real-Time Precise Point Positioning

“…In this case, it would be more appropriate to make use of a regional troposphere model based on real-time or near-real time (NRT) measurements. In this context, it may be effective to constrain the tropospheric delay using external information coming from NRT analysis of the regional GBAS network (Wielgosz et al 2011, Hadas et al 2013, Kalita et al 2014). …”