An improved GNSS tropospheric tomography method with the GPT2w model,GPS Solutions

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An improved GNSS tropospheric tomography method with the GPT2w model
GPS Solutions ( IF 4.5 ) Pub Date : 2020-03-16 , DOI: 10.1007/s10291-020-0974-4
Qingzhi Zhao , Wanqiang Yao , Yibin Yao , Xin Li

The GNSS tropospheric tomography technique has been proven to be a powerful tool for three-dimensional water vapor reconstruction. In most previous studies, the signals leaving the side face of the tomography area are ignored as having invalid information, which wastes valuable observations and decreases signal coverage of the research area. To include the contribution of such signals to the final tomographic result, an improved tropospheric tomography approach, which makes the most use of GNSS observations by combining the data derived from the empirical Global Pressure and Temperature 2 wet model, is proposed. Compared to the conventional method, the proposed method can adaptively use the signals penetrating from the model’s side face to the tomographic model, which increases the number of voxels crossed by rays and improves the stability of the tomography model. Numerical results in Hong Kong over the period of day of year 124–150, 2013 show that the internal accuracy of the tomographic model based on the proposed method increases by 9.8% when compared to the conventional method. The RMS errors of the integrated water vapor derived from the proposed method are 4.1 and 4.6 mm, respectively, while the values derived from the conventional method are 5.0 and 5.4 mm, respectively, when compared to the radiosonde and European Centre for Medium-Range Weather Forecasts (ECMWF) products. In addition, compared to the conventional method, the accuracy of the water vapor density profile derived from the tomographic result of the proposed method has been enhanced by 25% and 12.5% when the radiosonde and ECMWF data are considered as the reference, respectively. Such results indicate a good performance of the proposed method for GNSS troposphere tomography.

更新日期：2020-03-16

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