Atmospheric Water Vapor Content Inferred From GPS Data and Compared to a Global NWP Model and a Regional Climate Model
Conference poster, 2008
Radio based space geodetic methods are affected by the water vapor in the atmosphere.
The velocity of the propagating signal is reduced, depending on the value of the refractive
index. The atmospheric water vapor content, sometimes also called Integrated Water
Vapor (IWV), can be inferred from the estimated propagation delay, or the excess propagation
path often expressed in units of length. The observations are relative measurements
of time, which makes the methods interesting from a calibration point of view - since time
is the physical parameter that we can measure with the highest accuracy.
Since water vapor is difficult, and costly, to measure with a high temporal and spatial
resolution, given its characteristics of variability, researchers in the atmospheric sciences
have shown interest in using data from already existing ground-based continuously operating
GPS receivers. Time series of the IWV from specific sites are now longer than ten
years. For example, 20 sites in the Swedish GPS network have produced continuous data
since 1993/1994. In addition to GPS also additional global navigational satellite systems
(GNSS), such as the European Galileo and the finalization of the Russian GLONASS, will
in the future significantly improve the spatial sampling of the atmosphere, and also reduce
the relative influence of orbit errors for individual satellites.
We have analyzed ground-based GPS data acquired in Europe and Africa over the period
2001-2006. IWV results from the GPS data analysis are compared to the global Numerical
Weather Prediction (NWP) models from the European Center for Medium RangeWeather
Forecasting (ECMWF) as well as the regional climate model of the Rossby Center.
The overall goal for the possible use of GNSS data in climate research is to determine to
which extent these independent data can be used to discriminate between different climate
models - both in terms of absolute values as well as long term trends - thereby improving
the quality of the models and increasing the probability to produce realistic scenarios of
the future climate.