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Water vapor tomography is a promising technique for reconstructing the 4-Dmoisture field, which is important to the weather forecasting and nowcastingas well as to the numerical weather prediction. A near real-time 4-D watervapor tomographic system is developed in this study. GPS slant water vapor(SWV) observations are derived by a sliding time window strategy usingdouble-difference model and predicted orbits. Besides GPS SWV, surface watervapor measurements are also assimilated as real time observations into thetomographic system in order to improve the distribution of observations inthe lowest layers of tomographic grid. A 1-year term experiment in HongKong was carried out. The feasibility of the GPS data processing strategy isdemonstrated by the good agreement between the time series of GPS-derived Precipitable Water Vapor(PWV) and radio-sounding-derived PWV with a bias of 0.04 mm and a root-mean-squareerror (RMSE) of 1.75 mm. Using surface humidity observations in the tomographicsystem, the bias and RMSE between tomography and radiosonde data aredecreased by half in the ground level, but such improved effects weakengradually with the rise of altitude until becoming adverse above 4000 m.The overall bias is decreased from 0.17 to 0.13 g m<sup>−3</sup> and RMSE isreduced from 1.43 to 1.28 g m<sup>−3</sup>. By taking the correlation coefficient and RMSE between tomographyand radiosonde individual profile as the statistical measures, quality ofindividual profile is also improved as the success rate of tomographicsolution is increased from 44.44 to 63.82% while the failure rate isreduced from 55.56 to 36.18%

Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong