It is common practice to constrain the tropospheric zenith delay (TZD) parameter in a PPP solution. Depending on the quality of the temperature, pressure and relative humidity input data, I typically apply a constraint on the initial TZD value with a standard deviation between 5 and 20 cm. I never gave this pseudo-observation much attention until a problematic data set made me revisit this concept.
Processing of GNSS data collected on an airplane requires special handling of the troposphere. Due to large variations in altitude, the a priori hydrostatic and wet delays must be regularly updated since the tropospheric zenith delay (TZD) process noise would not allow proper tracking of those variations. However, the temperature and pressure reduction schemes employed in GPT2 do not seem well suited for this purpose, as demonstrated herein using data from the NGS kinematic challenge.
In precise point positioning (PPP) solutions, the wet part of the tropospheric zenith delay (TZD) is usually estimated along with position and receiver clock parameters. The residual TZD effect varies over time, but the magnitude of its variation is predictable to some extent. For this reason, process noise is added to the TZD variance at each epoch. But how should the process noise value be selected?