Braun J, Rocken C, Meertens C, Ware R (1999) Development of a water vapor tomography system using low cost L1 GPS receivers.Geophys Res Lett 33(7):L07304 Google Scholar
Böhm J, Niell A, Tregoning P, Schuh H (2006) Global mapping function (GMF): a new empirical mapping function based on numerical weather model data.Adv Atmos Sci 23(4):551–560 Google Scholar Cross Ref Bi Y, Mao J, Li C (2006) Preliminary results of 4-D water vapor tomography in the troposphere using GPS.Bevis M, Businger S, Herring TA, Rocken C, Anthes RA, Ware RH (1992) GPS meteorology: remote sensing of atmospheric water vapor using the global positioning system.In: Proceedings of the SPIE 9640, remote sensing of clouds and the atmosphere XX, 96400R. Benevides P, Catalao J, Nico G, Miranda PM (2015b) Inclusion of high resolution MODIS maps on a 3D tropospheric water vapor GPS tomography model.In: 2015 IEEE international geoscience and remote sensing symposium (IGARSS), pp 3607–3610 Google Scholar Benevides P, Nico G, Catalao J, Miranda P (2015a) Merging SAR interferometry and GPS tomography for high-resolution mapping of 3D tropospheric water vapour.
Física de la Tierra 26:65–79 Google Scholar
Benevides P, Catalão J, Miranda PM (2014) Experimental GNSS tomography study in Lisbon (Portugal). European Geosciences Union 25(8):1727–1734 Google Scholar Bender M, Raabe A (2007) A preconditions to ground based GPS water vapour tomography. Askne J, Nordius H (1987) Estimation of tropospheric delay for microwaves from surface weather data. Karlsruher Institut für Technologie (KIT) Google Scholar Alshawaf F (2013) Constructing water vapor maps by fusing InSAR, GNSS and WRF data. J Appl Meteorol Climatol 51(51):1855–1866 Google Scholar Cross Ref Adeyemi B, Joerg S (2012) Analysis of water vapor over Nigeria using radiosonde and satellite data. The PWV differences between tomography and GAMIT further indicate a good performance of the proposed algorithm with the values of RMS error and bias of 8.7 mm and 0.5 mm, respectively, while those of the traditional method are 12.6 mm and 0.9 mm, respectively. Water vapor profile comparison also shows that the RMS error and bias of the proposed algorithm are superior with average values of 1.17 g m −3 and 0.02 g m −3 to that of the conventional algorithm with values of 1.44 g m −3 and 0.03 g m −3, respectively. The compared result of integrated water vapor with those from radiosonde data reveals that the RMS error and bias of the proposed algorithm are 4.1 mm and 0.06 mm, respectively, while those of the conventional method are 4.8 mm and − 0.34 mm, respectively. At elevation angle masks of 10°, the number of satellite rays used has increased by 21.27% while the number of voxels transited by satellite rays has increased by 13.97% from 65.44 to 79.23% when adopting the TFM. The proposed algorithm is validated using the observed data collected over 31 days from the continuously operating reference system network of Zhejiang Province, China. A new troposphere tomography algorithm is proposed with a truncation factor model (TFM), while the ability of the TFM to calculate the sectional slant water vapor inside the tomographic area, derived from the receivers outside this area, has been verified. This becomes the focus of this work, which tries to use GNSS receivers located outside the tomographic region to participate in the establishment of a tomographic observation equation. This wastes valuable GNSS data and decreases the number of voxels traveled by satellite rays. For previous studies of global navigation satellite system (GNSS) troposphere tomography, only the GNSS observations derived from ground-based stations located inside the tomographic area were considered however, stations distributed outside the area of interest in a dense regional network were neglected.
0 kommentar(er)
