Ground GNSS Atmospheric Sensing - GNSS Remote Sensing: Theory, Methods and Applications - page 44

2800

y(t)=1.071*10 4 −4.094*t+65.090*sin(2* π *t+1.013)+0.224*sin( π *t+0.385)

2750

2700

2650

2600

2550

2500

2450

2400

2350

2300

1998 1999 2000 2001 2002 2003 2004 2005 2006

Time (year)

Fig. 3.8 ZTD time series at TOW2 station, Australia. The solid line is the fitting results, consisting
of a linear decrease and seasonal components

to the temperature and water vapor. Unfortunately, only fewer IGS sites have
meteorological instruments which can directly obtain the real ZWD or PWV.
If one calculated the ZWD or PWV using the meteorological data from the
European Centre for Medium-Range Weather Forecasts (ECMWF), it has some
differences relative to the real observation results with meteorological instrument
data (Hagemann et al. 2003 ). Therefore, we here analyze the variation and relation-
ship between atmospheric parameters at GPS stations equipped the meteorological
instruments. For example, the GPS station Wettzell (WETT), Germany has equipped
the meteorological instruments. The positions of humidity and temperature sensors
are the same as the GPS antenna, and the pressure sensor is 10.5 m below the GPS
antenna. The data frequencies of relative humidity, temperature and pressure are
all 15 min, and their accuracies are 1.5 % (relative to height), 0.3 ı C and 0.1 mbar,
respectively. The seasonal variations in ZTD are due primarily to the wet component
(ZWD), even though the wet delay is only 10 % of the total delay (ZTD). In addition,
the ZHD is proportional to the atmospheric pressure (Eq. 3.11 ) while the pressure is
mainly related to height, and therefore, the ZHD is almost constant, again showing
that the seasonal variations of ZTD are due primarily to the ZWD.

3.3.2.1 Secular ZTD Variations

The GPS ZTD time series have been analyzed for 4-12 years at globally distributed
150 GPS sites. Figure 3.8 shows an example of an original ZTD time series and the