Application of Algebraic Reconstruction Technique on the GNSS Water Vapor Tomography
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摘要: 全球导航卫星系统（GNSS）水汽层析反演技术是目前获取对流层水汽三维分布的重要方法。考虑到代数重构算法在迭代反演中具有节省计算机内存且计算稳定度高的优点，对代数重构算法在GNSS水汽层析中的应用进行了研究。研究结果表明，受水汽在对流层中的分布情况的影响，传统的加法代数重构算法在实际的层析解算中，会出现较大的重构误差，而乘法代数重构算法和调整了松弛参数向量的加法代数重构算法则大大提高了层析解算的精度；代数重构算法较附加约束条件的层析解算方法更易受到观测值误差的影响，但采用乘法代数重构算法可以获得优于加法代数重构算法的结果。
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关键词:
- 全球导航卫星系统(GNSS) /
- 代数重构算法 /
- 水汽 /
- 层析
Abstract: The GNSS water vapor tomography technique can be used to obtain spatially resolved humidity information about the troposphere. The application of this method in GNSS water vapor tomography is discussed in detail considering the need to save computer memory and in light of the high stability when calculating inversion with the algebraic reconstruction technique, The Algebraic Reconstruction Technique is used to construct the distribution of water vapor; experimental results indicate that the solution of the traditional ART shows large reconstruction error due to the distribution property of the water vapor in the troposphere, while the IART method which adapts a relaxation parameter vector gets a favorable solution. The MART method also shows the similar results. Compared inversion with constraints, the algebraic reconstruction technique method is more susceptible to the observation error. The solution generated by the MART method is better than that of the IART method. -
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图 1 层析区域站点分布及水平网格划分
Figure 1. Distribution of GNSS Sites and Horizontal Grids Defined in Tomography Region
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图 2 利用几种代数重构算法重构的大气湿折射率Nw的误差
Figure 2. Reconstruction Error of the Wet Refractivity Based on ART, IART and MART
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图 3 重构的大气湿折射率与网格真值的比较
Figure 3. Comparison Between the Reconstructed Field of Wet Refractivity and the Truth Value
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图 4 不同观测值误差下重构的大气湿折射率误差的均方根值
Figure 4. RMS of the Reconstructed Wet Refractivity Field at Different Observation Error Levels
表 1 不同解算方法重构误差的均值、均方根值
Table 1 Mean and RMS Value of the Reconstructed Error Based on Different Reconstruction Technique
方法 松弛因子 均值/(mm\5km-1) 均方根值/(mm\5km-1) 所有网格 有射线穿过的网格 所有网格 有射线穿过的网格 ART 约0.2 -0.50 0.39 8.40 7.47 IART 约0.1 -1.16 -0.26 5.64 4.16 MART 约0.15 -1.28 -0.32 5.78 4.24 附加约束条件的层析解算方法 / -0.99 -0.25 4.74 3.18 
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