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.