Water vapor content of the atmospheric low layer, up to
about 18 km, known as troposphere or neutral atmosphere,
affects GNSS (Global Navigation Satellite System) signals
by lowering their propagation velocities with respect to vacuum.
A diminished speed results in a time delay in the signal
propagation along the satellite-receiver path, that multiplied
by the vacuum speed of light adds an extra-distance
to the satellite-receiver geometrical one. This delay defines
a parameter which takes the name of Tropospheric Delay
(TD) and consists of an Hydrostatic (HD) and aWet (WD)
part. Anyway, if from the positioning point of view this delay
is just a systematic error to be removed, it puts forward
GNSS as a tool for the remote sensing of the troposphere
water vapor content.
The role of GNSS in meteorology is rapidly increasing; water
vapor plays a crucial role in atmospheric processes that
act over a wide range of temporal and spatial scales, from
global climate to micrometeorology [16] and GNSS data can
be extremely useful for the purpose of a multi-instrumental
investigation; indeed it was used to calibrate and validate
other instruments [26] or methodologies [87], but probably
one of the most impactful application is related to the assimilation
of GNSS data into the meteorological models [8]
[42] [19].
In this study, a global analysis of the role of the GPS data in
the field of meteorology was carried out. A focus was kept
on the assimilation of data in NWP Models, also according
to the cooperation with the Institute of Atmospheric Sciences
and Climate of National Research Council of Italy,
which included this work in the frame of a collaboration\ud
with the Department of Italian Civil Protection.
The main aim of this thesis is to find parameters able to
support the analysis and forecast of intense meteorological
events.
To do this, a comparative analysis was carried out between
GPS outputs and other Precipitable Water Vapor (PWV)
measurement instruments; results show great consistency
between the data (St. Dev.1cm).
Another test was performed on the assimilation in NWP
Models, in particular RAMS Model; in this case it has been
found a noticeable impact (20-30% improvement) on ZTD
and IWV for short term forecast.
As for the Near Real Time (NRT) processing, results obtained
are encouraging with a St. Dev.< 1cm with respect
to post-processing (PP).
To sum up, results provide an overall assessment of the
data quality obtained through GPS post-processing and a
milestone for NRT processing, also in view of early warning
systems
