The water vapor content in the atmosphere can be reconstructed using the all-weather condition troposphere tomography technique. In common troposphere tomography, the water vapor of each voxel is represented by an unknown parameter. This means that when the desired spatial resolution is high or study area is large, there will be a huge number of unknown parameters in the problem that need to be solved. This defect can reduce the accuracy of troposphere tomography results. In order to overcome

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... is problem, an optimal voxel-based troposphere tomography using the Weather Research and Forecasting (WRF) model is proposed. The new approach reduces the number of unknown parameters, the number of empty voxels and the role of constraints required to enhance the spatial resolution of tomography results in required areas. Furthermore, the effect of considering the topography of the study area in the tomography model is examined. The obtained water vapor is validated by radiosonde observations and Global Positioning System (GPS) positioning results. Comparison of the results with the radiosonde observations shows that using the WRF model outputs and topography of the area can reduce the Root Mean Square Error (RMSE) by 0.803 gr/m3. Validation using positioning shows that in wet weather conditions, the WRF model outputs and topography reduce the RMSE of the east, north and up components by about 17.42, 10.46 and 20.03 mm, which are equivalent to 46.01%, 35.78% and 53.93%, respectively.