Funded by the Austrian Science Fund (FWF, ORD-68), we calculate and provide tropospheric delays and models as open access data. This server replaces ggosatm.hg.tuwien.ac.at, which has been shut down in April 2019. The portfolio of tropospheric delays and models provided here is significantly enhanced compared to the previous server. A manual on the usage and application of the troposphere products can be found in the readme.txt.
Troposphere delay model data
- GRID : for regular global grids in different resolutions.
- GNSS : for the exact locations of more than 500 IGS (International GNSS Service) stations.
- VLBI : for the exact locations of more than 200 IVS (International VLBI Service for Geodesy and Astrometry) stations.
- DORIS : for the exact locations of ~ 200 IDS (International DORIS Service) stations.
- SLR : for the exact locations of ~ 200 ILRS (International Laser Ranging Service) stations.
Atmospheric pressure loading (APL) data
- TIDAL : tidal APL data for the regular global 1°x1° grid as well as for the various stations.
- GRID : non-tidal APL data on a regular global 1°x1° grid.
- GNSS : non-tidal APL data for the exact locations of more than 500 IGS (International GNSS Service) stations.
- VLBI : non-tidal APL data for the exact locations of more than 200 IVS (International VLBI Service for Geodesy and Astrometry) stations.
- DORIS : non-tidal APL data for the exact locations of ~200 IDS (International DORIS Service) stations.
- SLR : non-tidal APL data for the exact locations of ~200 ILRS (International Laser Ranging Service) stations.
- readme.txt. Please mind that recent observations might be missing due to delayed provision of the respective VLBI data.
- readme.txt: contains a description about the usage of the homepage
- VMF1: Troposphere mapping functions for GPS and VLBI from ECMWF operational analysis data (Böhm et al., 2006a)
- VMF1 Forecast: Forecast Vienna Mapping Functions 1 for real-time analysis of space geodetic observations (Böhm et al., 2009)
- VMF3 & GPT3: VMF3/GPT3: refined discrete and empirical troposphere mapping functions (Landskron & Böhm, 2018)
- GMF: Global Mapping Function (GMF): A new empirical mapping function based on numerical weather model data (Böhm et al., 2006b)
- GPT: A Global Model of Pressure and Temperature for Geodetic Applications (Böhm et al., 2007)
- GPT2: GPT2: Empirical slant delay model for radio space geodetic techniques (Lagler et al., 2013)
- GPT2w: Development of an improved empirical model for slant delays in the troposphere (GPT2w) (Böhm et al., 2015)
- GRAD: Refined discrete and empirical horizontal gradients in VLBI analysis (Landskron & Böhm, 2018)
- RADIATE: Application of ray-traced tropospheric slant delays to geodetic VLBI analysis (Hofmeister & Böhm, 2017)
Structure of the troposphere delay models
The following two organigrams give an overview of the structure of the troposphere products.
VMF3 data at Wettzell (Germany)
This plot shows a comparison of VMF3 operational data (VMF3OP, blue) with VMF3 forecast data (VMF3FC, red) for the Geodetic Observatory Wettzell in Germany over the last 4 weeks. The red line is always two days ahead of the blue line, since VMF3OP is always published for the previous day and VMF3FC for the upcoming day.
- For a large number of mjd's, the application of gpt3_1.m / gpt3_5.m / gpt3_1.f90 can be very time-consuming, in particular the 1°x1° version. The reason for that is that the underlying data grid is opened and closed in every run. When using MATLAB, the routines gpt3_1_fast.m / gpt3_5_fast.m together with gpt3_1_fast_readGrid.m / gpt3_5_fast_readGrid.m can be used instead.
- For VLBI, GNSS or DORIS analysis, the site-wise VMF3 data is of considerably higher quality than bilinearly interpolated grid-wise VMF3 data. The cause lies in the conversion of the zenith delays from grid height to station height. For the hydrostatic part, an empirical model is used for the conversion, but for the wet part only a rule of thumb is applied, due to a lack of more accurate models.
- The site-wise GRAD data can be very well approximated by bilinear interpolations of the 1°x1° GRAD grid. Using the 5°x5° GRAD grid instead may be distinctively faster, but the quality of the interpolated gradients (particularly the wet part) is significantly lower.
Johannes Böhm, Daniel Landskron, Janina Boisits
Research Division Higher Geodesy
Department of Geodesy and Geoinformation (E120-4)
Vienna University of Technology (TU Wien)
A-1040 Vienna, Austria