On the Study of Influences of Different Factors on the Rapid Tropospheric Tomography

  • 发布时间:[2020-04-30] 来源:[学院] 点击量:[327]
作者: Liu, WX (Liu, Wenxuan); Lou, YD (Lou, Yidong); Zhang, WX (Zhang, Weixing); Huang, JF (Huang, Jinfang); Zhou, YZ (Zhou, Yaozong); Zhang, HS (Zhang, Haoshan)
来源出版物: REMOTE SENSING  : 11  : 13  文献号: 1545  DOI: 10.3390/rs11131545  出版年: JUL 1 2019 
摘要: A rapid tropospheric tomography system was developed by using algebraic reconstruction technique. Influences of different factors on the tomographic results, including the ground meteorological data, the multi-Global Navigation Satellite System (GNSS) observations, the ground station distribution and the tomographic horizontal resolution, were systematically investigated. In order to exclude the impacts from discrepancies of water vapor information between input observations and references on the tomographic results, the latest reanalysis products, ERA5, which were taken as references for result evaluations, were used to simulate slant wet delay (SWD) observations at GNSS stations. Besides, the slant delays derived from GNSS processing were also used to evaluate the reliability of simulated observations. Tomography results show that the input both SWD and ground meteorological data could improve the tomographic results where SWD mainly improve the results at middle layers (500 to 5000 m, namely 2 to 16 layer) and ground meteorological data could improve the humidity fields at bottom layers further (0 to 500 m, namely 0 to 2 layer). Compared to the usage of Global Positioning System (GPS) only SWD, the inclusion of multi-GNSS SWD does not significantly improve the tomographic results at all layers due to the almost unchanged dispersion of puncture points of GNSS signals. However, increases in the ground GNSS stations can benefit the tomography, with improvements of more than 10% at bottom and middle layers. Higher tomographic horizontal resolution can further slightly improve the tomographic results (about 3-6% from 0.5 to 0.25 degrees), which, however, will also increase the computational burden at the same time.

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