Abstract
Traditional troposphere tomography method cannot use the GNSS signals penetrating from the side face of research area, which not only decreases the utilization rate of GNSS observation but also leads to a low percentage of voxels crossed by rays. In order to overcome this issue, the GNSS signals penetrating from the model’s side face are also used to build the observation equation by introducing the truncation coefficient in this paper. Due to the fact that the tomography modeling is consists of various equations, including observation equation (using signals from the side and top faces of research area to build equations), horizontal and vertical equations, how to determine the weightings of different equations is a key to obtain the reliable tomographic result. Therefore, a method is proposed to determine the weightings of various equations based on the variance component analysis (VCA). The data from Satellite Positioning Reference Station Network (SatRef) of Hong Kong over the period of 27 days is selected for the tomography experiment. The tomographic result shows that the proposed method is of ability to obtain a good quality. Comparing to the traditional method, the utilization rate and number of voxels crossed by rays have been improved by 32.21 and 12.23%, respectively. When compared to the radiosonde data, the RMS error of the reconstructed integral water vapor (IWV) derived from the proposed method (4.2 mm) superior to that from the traditional method (5.2 mm). The comparison of water vapor profiles also shows that the proposed method with a RMS value of 1.30 g/m3, is smaller than that of traditional method with a value of 1.58 g/m3, and the accuracy of tomographic result based on the proposed method is increased by 17.7%.
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References
Bevis M, Businger S, Herring T, Rocken C et al (1992) GPS meteorology-remote sensing of atmospheric water vapor using the global positioning system. J Geophys Res 97(D14):15787–15801
Rocken C, Ware R, Van Hove T et al (1993) Sensing atmospheric water vapor with the global positioning system. Geophys Res Lett 20(23):2631–2634
Duan J, Bevis M, Fang P et al (1996) GPS meteorology: direct estimation of the absolute value of precipitable water. J Appl Meteorol 35(6):830–838
Emardson TR, Johansson J, Elgered G (2000) The systematic behavior of water vapor estimates using four years of GPS observations. IEEE Trans Geosci Remote Sens 38(1):324–329
Gendt G, Dick G, Reigber C et al (2004) Near real time GPS water vapor monitoring for numerical weather prediction in Germany. J Mereorol Soc Jpn 82(1B):361–370
Smith TL, Benjamin SG, Gutman SI et al (2007) Short-range forecast impact from assimilation of GPS-IPW observations into the rapid update cycle. Mon Weather Rev 135(8):2914–2930
Raja MRV, Gutman SI, Yoe JG et al (2008) The validation of AIRS retrievals of integrated precipitable water vapor using measurements from a network of ground-based GPS receivers over the contiguous United States. J Atmos Ocean Technol 25(3):416–428
de Haan S, Holleman I, Holtslag AA (2009) Real-time water vapor maps from a GPS surface network: construction, validation, and applications. J Appl Meteorol Climatol 48(7):1302–1316
Lee SW, Kouba J, Schutz B et al (2013) Monitoring precipitable water vapor in real-time using global navigation satellite systems. J Geodesy 87(10–12):923–934
Brenot H, Wautelet G, Warnant R et al (2014) GNSS meteorology and impact on NRT position. In: European navigation conference (ENC) GNSS 2014
Chen B, Liu Z (2014) Voxel-optimized regional water vapor tomography and comparison with radiosonde and numerical weather model. J Geodesy 88(7):691–703
Braun J, Rocken C, Meertens C et al (1999) Development of a water vapor tomography system using low cost L1 GPS receivers. In: Ninth ARM science team meeting, US Department of Energy, San Antonio, Tex, pp 22–26
Radon J (1917) Über die Bestimmung von Funktionen durch ihre In-te-gral-werte längs gewisser Mannigfaltigkeiten. Comput Tomogr 69:262–277
Flores A, Ruffini G, Rius A (2000) 4D tropospheric tomography using GPS slant wet delays. Annales Geophysicae 18(2):223–234
Alshawaf F (2013) Constructing water vapor maps by fusing InSAR, GNSS and WRF data. Karlsruhe, Karlsruher Institut für Technologie (KIT), Dissertation
Benevides P, Nico G, Catalao J et al (2015) Merging SAR interferometry and GPS tomography for high-resolution mapping of 3D tropospheric water vapour. In: IEEE international geoscience and remote sensing symposium (IGARSS), pp 3607–3610
Rohm W, Bosy J (2011) The verification of GNSS tropospheric tomography model in a mountainous area. Adv Space Res 47(10):1721–1730
Van Baelen J, Reverdy M, Tridon F et al (2011) On the relationship between water vapour field evolution and the life cycle of precipitation systems. Q J R Meteorol Soc 137(S1):204–223
Benevides P, Catalão J, Miranda PM (2014) Experimental GNSS tomography study in Lisbon (Portugal). Física de la Tierra 26:65–79
Hirahara K (2000) Local GPS tropospheric tomography. Earth Planets Space 52(11):935–939
Troller M, Burki B, Cocard M, Geiger A et al (2002) 3-D refractivity field from GPS double difference tomography. Geophys Res Lett 29:2149–2152
Perler D, Geiger A, Hurter F (2011) 4D GPS water vapour tomography: new parameterized approaches. J Geophys Res 85:539–550
Skone S, Hoyle V (2005) Troposphere modeling in a regional GPS network. Positioning 4(1&2):230–239
Bender M, Raabe A (2007) Preconditions to ground based GPS water vapour tomography. Ann Geophys 25(8):1727–1734
Wen D, Liu S, Tang P (2010) Tomographic reconstruction of ionospheric electron density based on constrained algebraic reconstruction technique. GPS Solutions 14(4):375–380
Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica 55(2):251–276
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366):427–431
Dickey DA, Fuller WA (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49(4):1057–1072
Acknowledgements
The authors would like to thank IGAR for providing access to the web-based IGAR data. The Lands Department of HKSAR is also acknowledge for providing GPS data from the Hong Kong Satellite Positioning Reference Station Network (SatRef) and the corresponding meteorological data. This research was supported by the Excellent Youth Science and Technology Fund Project of Xi’an University of Science and Technology (2018YQ3-15) and the Startup Foundation for Doctor of Xi’an University of Science and Technology (2017QDJ041).
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Zhao, Q., Yao, Y., Xin, L. (2018). A Troposphere Tomography Method by Combining the Truncation Coefficient and Variance Component Analysis. In: Sun, J., Yang, C., Guo, S. (eds) China Satellite Navigation Conference (CSNC) 2018 Proceedings. CSNC 2018. Lecture Notes in Electrical Engineering, vol 497. Springer, Singapore. https://doi.org/10.1007/978-981-13-0005-9_1
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