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An improved tropospheric tomography method based on the dynamic node parameterized algorithm
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Abstract
Wenyuan Zhang, Shubi Zhang, Nan Ding and Pengxu Ma.
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In the general tropospheric tomography model, the tomographic area is divided into a large number of voxels, which provides convenience for reconstructing tomographic observation equations. However, due to the defect of GNSS acquisition geometry, there are plenty of empty voxels for any tomographic epoch. Moreover, an unreasonable assumption that water vapor density is constant within a voxel was imposed on the tomographic model. In this study, we proposed an improved method based on the dynamic node parameterized algorithm to solve both key problems. The proposed approach first tries to select effective GNSS signals and determines the dynamic scope of the tomographic area using the dynamic algorithm. The parameterization of the tomography model is performed by a cubic spline formula and Gauss weighted function. Additionally, a piecewise linear fitting method based on Newton-Cotes interpolation is introduced to estimate the tomographic observation of slant water vapor (SWV). The experimental results show that the average number of effective signals increased by 32.33 % and the mean RMSE of the tomographic results is decreased by 45 % with the proposed method. Further, compared with the tomographic results of the general method, the improved solutions have a more centralized distribution and a smaller bias.
Obsahuje bibliografii
In the general tropospheric tomography model, the tomographic area is divided into a large number of voxels, which provides convenience for reconstructing tomographic observation equations. However, due to the defect of GNSS acquisition geometry, there are plenty of empty voxels for any tomographic epoch. Moreover, an unreasonable assumption that water vapor density is constant within a voxel was imposed on the tomographic model. In this study, we proposed an improved method based on the dynamic node parameterized algorithm to solve both key problems. The proposed approach first tries to select effective GNSS signals and determines the dynamic scope of the tomographic area using the dynamic algorithm. The parameterization of the tomography model is performed by a cubic spline formula and Gauss weighted function. Additionally, a piecewise linear fitting method based on Newton-Cotes interpolation is introduced to estimate the tomographic observation of slant water vapor (SWV). The experimental results show that the average number of effective signals increased by 32.33 % and the mean RMSE of the tomographic results is decreased by 45 % with the proposed method. Further, compared with the tomographic results of the general method, the improved solutions have a more centralized distribution and a smaller bias.