On the determination of atmospheric water vapor content from ground-based GPS

Atmospheric water vapor has a fundamental role in radiative transfer, atmospheric dynamics, and the hydrological cycle. Despite the considerable progress in the Earth’s observing system, it is still inadequately characterized because of its high spatial and temporal variability and the lack of long-term homogeneous observational data. The availability of long records of high-temporal resolution ground-based GPS measurements of total column water vapor (TCWV) offers a unique opportunity for a variety of atmospheric research studies. The potential of such measurements that are not yet fully exploited are here presented and discussed. In particular, the following issues are outlined: (i) the characteristics of high-temporal resolution TCWV data (i.e., time behavior, spectral features, long-term memory), (ii) their usefulness for hydrological applications at basin/regional level (i.e., monitoring weather extremes, estimates of water balance components), and (iii) their use in climatological studies (i.e., long-term trends, computation of precipitation efficiency and monitoring meteorological wet/dry spells). For these purposes, sample stations in U.S. and South America from the SuomiNet and IGS networks, which have rather continuous measurements for the last decade and a few missing values, have been considered and data analyzed. Comparisons with reanalysis products have been carried out and are here illustrated. Novel findings have been the subject of four publications in the international scientific journals; they are here presented and discussed. The overall results suggest that for a comprehensive monitoring of a region, a GPS network that encloses the area of concern equipped with ground meteorological sensors is suitable and desirable. GPS observations, jointly with other meteorological data, can provide an accurate estimate of the imbalance between evapotranspiration and precipitation that is of interest for drought assessment, and of the terrestrial water storage rate of change that is known to be difficult to measure. The expected (positive) impact of ground-based GPS data assimilation into numerical weather prediction models on the short-term forecasts skill should be operationally verified, as well as the advantages of having estimates of precipitation efficiency and data with long-term memory features.