[1] Variations in the refractive index of the atmosphere cause variations in satellite-based interferometric synthetic aperture radar (InSAR) observations. We can mitigate tropospheric effects by averaging N-independent interferograms. Because the neutral atmosphere is uncorrelated at timescales longer than 1 day, using this technique statistically reduces the variance, s 2 , of the noise by a factor of N. Using zenith neutral atmospheric delays from Global Positioning System (GPS) data from the Southern California Integrated GPS Network, we find that the average variance depends on the distance between observations, L, and height difference, H, as s = c L a + kH with estimated values for c, a, and k of about 2.5, 0.5, and 4.8, respectively, where s is in mm and L and H are in km. We expect that the value of a is largely site-independent but the value of c will depend on the water vapor variability of the area of interest. This model is valid over a range of L between approximately 10 and 800 km. Height differences between 0 and 3 km have been used in this analysis. For distances of 100 and 10 km with negligible height differences, s is estimated to be approximately 25 and 8 mm, respectively. For a given orbit revisit time and image archive duration, we calculate the number and duration (assumed constant) of interferograms required to achieve a desired sensitivity to deformation rate at a given length scale. Assuming neutral atmosphere is the dominant source of noise, a 30°look angle, and an image revisit time of 7 days, detection of a deformation rate of 1 mm yr À1 over distances of 10 km requires about 2.2 years of continuous observations. Given our results, we suggest a data covariance structure to use when using InSAR data to constrain geophysical models.

Abstract. Three months of continuous data from the Global Positioning System (GPS) using 20 sites in Sweden and 5 sites in Finland have been used to estimate the integrated amount of atmospheric water vapor. The quality of the data has been assessed by comparisons with a microwave radiometer (water vapor radiometer (YVVR)) at the Onsala Space Observatory and with data from four different radiosonde stations. We found the agreement in integrated water vapor (IWV) between the GPS estimates and the radiometer data to be 1-2 kg/m 2 in terms of daily root-mean-square (rms) differences. A major part of these rms differences were caused by a bias between the data sets. This bias (WVR-GPS) varied from day to day between -1.0 and +2.5 kg/m 2 with a mean value of +1.3 kg/m 2.Comparisons with radiosonde data showed rms differences around or slightly above 2 kg/m 2 for each station using the entire 3 month data set. Also here the GPS estimates were, on the average, below the radiosonde results. We show that the radomes used to protect the GPS antennas are likely to cause a large part of the observed bias. Spatial structure functions were calculated by using the GPS and the radiosonde data. An overall consistency between the GPS-based and the radiosonde-based structure functions indicates that the spatial correlations between the GPS estimates are not affected by the estimation process used in the GPS data analysis.

A ground-based network of Global Positioning System (GPS) receivers can be used to determine the