Abstract
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 this 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.
Highlights
The accurate reconstruction of water vapor is very important for weather forecasting and other applications, such as the early warning of weather disasters
Many researchers have tried to improve the accuracy of the results by optimizing different aspects of the voxel-based troposphere tomography: (1) improving the inversion process [6,7], (2) reformatting the model geometry [8,9,10,11,12], (3) applying advance optimization techniques [13], (4) using the signals penetrating into the side face of the tomography model and using the data from Global Navigation Satellites System (GNSS) observations outside the study region [8,10,14,15,16]
An applicable idea based on the Weather Research and Forecasting (WRF) model outputs was presented to optimize the troposphere tomography technique by merging voxels
Summary
The accurate reconstruction of water vapor is very important for weather forecasting and other applications, such as the early warning of weather disasters. In most of the currently used models [17] this method uses only one unknown parameter to represent the water vapor in each voxel and cannot compute the water vapor distribution within a voxel. Another issue related to model geometry is topography, which is usually not present in the voxel parametrization [7]. The number of voxels or number of unknown parameters increases when there is a need for high spatial resolution water vapor reconstruction, which is of interest in complex terrains. Increasing the number of unknown parameters decreases the degree of freedom of the problem, decreases the accuracy of the results and may increase the need to use constraints to solve the problem
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