To select the interpolation algorithm for the refinement of Precipitable Water Vapor (PWV),this paper systematically analyzes three interpolation methods including the linear interpolation triangulation,the Kriging interpolation and the Inverse Distance Weighting (IDW) interpolation,and then proposes an improved IDW interpolation approach.First,both the influence of GNSS station distance and the distribution characteristics of atmospheric water vapor on the interpolation result is analyzed,which is then used to optimize the interpolation parameters thus make the interpolation result close to the high-precision observation value.Second,this approach is tested using GNSS data of Xuzhou continuously operated reference stations as well as the radiosonde data during the period of May to July 2017.The results demonstrate that the improved IDW interpolation approach outperforms the above three classical interpolation methods in standard deviation,mean absolute error,mean relative error,and Root Mean Square Error (RMSE).Specifically,the RMSE is lowered by 14.88%,15.70% and 4.12%,compared with the linear interpolation triangulation,the Kriging and the IDW interpolation,respectively.Moreover,the proposed interpolation approach has excellent ability in reconstructing the high-resolution atmospheric water vapor distribution map during storms,which can significantly reduce the interpolation error caused by the uneven distribution of sampling sites and the precipitation surge.The comparisons indicate that the improved IDW interpolation approach is conducive to reconstruct the high-resolution atmospheric water vapor distribution map for areas with sparse GNSS station network,thus to improve the capacity of extreme weather monitoring.