The TropGrid2 standard tropospheric correction model

Development of an improved empirical model for slant delays in the troposphere (GPT2w)

“…1, ibid.) provides statistics for several years illustrating that the RMS values change by about 2 mm over the years depending on the distribution of IGS antennas available. Considering this distribution of RMS values, our results agree reasonably well with those for TropGrid2 by Schüler (2014). The RMS values in his study, however, contain systematic contributions from the biases between the time series.…”

“…4, bottom) with IGS data cover a range of 1.5 to 6.1 cm and are of typical low magnitude for dry (sub-) polar regions, innercontinental areas, and high-terrain stations such as the two North-Andean receivers located above 2,500 m in Colombia and Ecuador. Significant non-climatological variability in zenith total delays, presumably related to westerly cyclone-anticyclone patterns or monsoon activity, produces large RMS values beyond 4 cm for 100 midlatitude and subtropical stations at an average height of 149 m. The confinement of such peak disparities to altitudes near sea level has already been exemplified by akin comparisons in Schüler (2014). Despite the obvious limitations of our blind model, the majority of sites in the Asian-Pacific region profit from using GPT2w instead of GPT2 (with low-spatial resolution and inadequate wet delays) or the ESA model that disregards semiannual harmonics.…”

“…Schüler (2014; Fig. 1, ibid.) provides statistics for several years illustrating that the RMS values change by about 2 mm over the years depending on the distribution of IGS antennas available.…”

“…With GPT2w, the vertical extrapolation of e s adheres to (5), utilizing the GPT2w-inherent values of the water vapor decrease factor k. Pressure reductions conform to GPT2's use of exponential trend coefficients calculated from grid point-wise virtual temperature information. Such isothermal scale heights may be alternatively adjusted for adiabatic effects, but the benefit of this approach on the side of delay predictions is not entirely conclusive (Schüler, 2014). Table 1 summarizes the main features of the newly suggested model, which can be compared to Tab.…”

“…Widely adopted utilities including proxies for the zenith wet delays are RTCA-MOPS (1999), originally called UNB3 (Collins et al 1996), the European Space Agency Galileo User Receiver model (ESA GAL-TROPO) (Krueger et al 2004(Krueger et al , 2005Martellucci 2012) and the concurrently developed TropGrid model (Krueger et al 2004) with a recent update called TropGrid2 by Schüler (2014). The ESA model and both TropGrid versions account for annual and diurnal variations of the underlying parameters.…”