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In-Depth Information
the data analysis will also contain information about the tropospheric delay, which is
closely related to the IWV (see Sect.
3.2.2
). Several studies investigated the accuracy
of the zenith wet delays and IWV estimated fromVLBI and GNSS (e.g. Herring et al.
1990
, Tralli and Lichten
1990
, Bevis et al.
1992
, Snajdrova et al.
2006
,Tekeetal.
2011
), and these show that it is on the same level or better then that of other techniques.
Thus there exists a large interest in applying space geodetic techniques, especially
GNSS, for atmospheric studies. For example, zenith wet delays can be used to study
climate trends (Sect.
6.1
), or assimilated into numerical weather prediction models to
improve weather forecasts (Sect.
6.2
). With wet delays estimated from a local GNSS
network one can even attempt to estimate the 3D structure of the atmospheric water
vapor by applying tomographic methods (Sect.
6.3
).
6.1 Long-Term Water Vapor Trends
Since the zenith wet delay is closely related to the integrated water vapor content
(see Sect.
3.2.2
), we can analyze
L
z
w
estimated from space geodetic techniques to
study the variations of the atmospheric water vapor content in time. For example,
it is possible to study diurnal and seasonal variations as well as long term trends in
the water vapor content. Such information is of great interest in climatology since
the water vapor content is closely related to the temperature. Climate models typi-
cally predict that the average relative humidity remains constant as the temperature
changes (Trenberth et al.
2003
). Since the saturation water vapor pressure depends
approximately exponentially on the temperature, this means that a a change in the
temperature will cause a corresponding change in the water vapor content. It is pre-
dicted that an increase in temperature of 1 K will increase the water vapor content by
6-7 % (Trenberth et al.
2003
; Bengtsson et al.
2004
). It is important to monitor the
water vapor content since water vapor is a greenhouse gas, in fact the most impor-
tant one. Additionally, higher water vapor content can also indicate an intensified
hydrological cycle, including increased precipitation.
Several studies have calculated long-term trends in
Δ
L
z
w
(or IWV) estimated from
GNSS and VLBI, e.g. Gradinarsky et al. (
2002
); Jin et al. (
2007
); Steigenberger et al.
(
2007
); Heinkelmann et al. (
2007
); Ning and Elgered (
2012
). An example of
Δ
L
z
w
trends calculated from ten years of GPS data in Sweden and Finland is shown in
Fig.
15
. For more details, see Nilsson and Elgered (
2008
).
Δ
6.2 GNSS Meteorology
Water vapor is a very important parameter in meteorology and in order to get accu-
rate weather forecasts it is very important to have accurate measurements of the
water vapor content. A problem is that the water vapor content is highly variable
in both space and time, and traditional instruments (e.g. radiosondes) do not pro-
