The vertical structure of water vapor in atmosphere is one of the initial information of numerical weather forecast model. Because of the strong variation of water vapor in atmosphere and limited spatio-temporal solutions of traditional observation technique, the initial water vapor field of numerical weather forecast model can not accurately be described. At present, using GPS slant observations to study water vapor profile is very popular in the world. Using slant water vapor(SWV) observations from Shanghai GPS network, we diagnose the three-dimensional(3D) water vapor structure over Shanghai area firstly in China. In water vapor tomography, Gauss weighted function is used as horizontal constraint, the output of numerical forecast is used as apriori information, and boundary condition is also considered. For the problem without exact apriori weights for observations, estimation of variance components is introduced firstly in water vapor tomography to determine posteriori weights. Robust estimation is chosen for reducing the effect of blunders on solutions. For the descending characteristic of water vapor with height increasing, non-equal weights are used along vertical direction. Comparisons between tomography results and the profile provided by numerical model (MM5) show that the forecasted moisture fields of MM5 can be improved obviously by GPS slant water vapor. Using GPS slant observations to study 3D structure of atmosphere in near real-time is very important for improving initial water vapor field of short-term weather forecast and enhancing the accuracy of numerical weather forecast.