Phase delay caused by atmospheric effects due to spatial and temporal variations of pressure, temperature, and water vapor content is one of the major errors ources in estimation of ground deformation by interferometric synthetic aperture radar (InSAR). Therefore, accuracy of ground deformation measurement is highly contingent on the robustness of the atmospheric correction techniques. These techniques rely eitheron auxiliary data such as numerical weather models or on the analysis of the interferometric phase itself. The accuracyin phase delays estimation of mixing effectsof turbulent delay in atmosphere and stratified delay in lower troposphere is a key factor in determination of performanceof each technique. Hence, the performance evaluation of the techniques is required in order toassess their potentials, robustness and limitations. This paper analyzes and evaluates the performance of four numerical weather models (i.e., ERA-Interim, ERA5, MERRA2 and WRF) and two phase-based techniques (i.e., linear and power law) to estimate phase delay using Sentinel-1A/B data over the Corvara landslide located in the Alps. The GPS data and GACOS product were used to validate the results. We generally found that ERA5 outperformed among other weather models with a phase standard deviation reduction of 77.7%(with respect to the InSAR phase), a correlation coefficient of 0.86 (between InSAR phase and estimated tropospheric delay) and a less significant error in the velocity estimation of the landslide.