Atmosphere causes delay distortions in synthetic aperture radar images. Numerical weather prediction models, which compute the forecast stepwise, are beneficial in correcting this distortion. After initialization, the model needs time to reach a balanced state, such that first prediction steps contain errors. The imbalance causes false predicted precipitation, which then affects the water vapor distribution. Correspondingly, the predicted zenith path delay (ZPD), which depends on this distribution, is affected by the initial imbalances. The digital filtering initialization (DFI) technique reduces these imbalances and the ZPD prediction disturbances, respectively. The objective of this paper is the accuracy gain for ZPD predictions, which is achieved by this technique. For the accuracy gain investigation, predicted ZPD time series of the weather research and forecasting (WRF) model with and without DFI are compared against Global Navigation Satellite System (GNSS)-derived time series from 233 GNSS stations mainly located in Germany. Three conclusions are found. First, the experiment confirms that the DFI technique improves the precipitation forecast. Second, the corresponding accuracy gain, i.e., the bias of ZPD predictions, improves by about 13%. Third, the accuracy gain is only valid for the first 4 h of the prediction.