Space geodesy techniques (SAR interferometry and GNSS) have recently emerged as an important tool for mapping regional surface deformations due to tectonic movements. A limiting factor to this technique is the effect of the troposphere, as horizontal and vertical surface velocities are of the order of a few mm yr⁻¹, and high accuracy (to mm level) is essential. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be successfully removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Several methods have been proposed, including Global Navigation Satellite System (GNSS) zenithal delay estimations, satellite multispectral imagery analysis, and empirical phase/topography estimations. These methods have their limitations, as they rely either on local data assimilation, which is rarely available, or on empirical estimations which are difficult in situations where deformation and topography are correlated. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and minimization of atmospheric phase delay, so that the remaining signal represents the deformation mostly due to tectonic or other geophysical processes. In this context, the current PhD Thesis aims to investigate the extent to which a high-resolution weather model, such as WRF, can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model initially is operated with varying parameterization in order to demonstrate the best possible configuration for our study, with GNSS measurements providing a benchmark of real atmospheric conditions. In the next phase, the two datasets (predicted and observed) are compared and statistically evaluated for a period of one year, in order to investigate the extent to which meteorological parameters that affect ZTD, can be simulated accurately by the model under different weather conditions. Finally, a novel methodology is tested, in which ZTD maps produced from WRF and validated with GNSS measurements in the first phase of the experiment are used as a correction method to eliminate the tropospheric effect from selected InSAR interferograms. Results show that a high-resolution weather model which is fine-tuned at the local scale can provide a valuable tool for the tropospheric correction of InSAR remote sensing data.