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Advances in Meteorology
Volume 2017 (2017), Article ID 3782687, 13 pages
Research Article

Seasonal Multifactor Modelling of Weighted-Mean Temperature for Ground-Based GNSS Meteorology in Hunan, China

1Hunan Provincial Key Laboratory of Clean Coal Resources Utilization and Mine Environmental Protection, Hunan University of Science and Technology, Xiangtan 411201, China
2SPACE Research Centre, School of Science, RMIT University, Melbourne, VIC 3000, Australia
3Hunan Meteorological Observatory, Changsha 410118, China
4School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

Correspondence should be addressed to Kefei Zhang

Received 25 December 2016; Accepted 15 February 2017; Published 2 March 2017

Academic Editor: Stefania Bonafoni

Copyright © 2017 Li Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional models (RTMs) that are a fitting function of multiple meteorological factors (, , and ). One-factor, two-factor, and three-factor RTMs were assessed by comparing their against the radiosonde-derived (as the truth) during the period of 2013-2014. Statistical results showed that the bias and RMS of the one-factor RTM, in comparison to the BTM result, were reduced by 88% and 28%, respectively. The two-factor and three-factor RTMs showed similar accuracy and both outperformed the one-factor RTM, with an improvement of 7% in RMS. The bias and RMS of all the four seasonal two-factor RTMs were smaller than the yearly two-factor RTM, with the improvements of 3%, 10%, 2%, and 3% in RMS. The improvement of the conversion factors in mean bias and RMS resulting from the seasonal two-factor RTM is 92% and 31%. The bias and RMS of the PWV resulting from the seasonal two-factor RTM are improved by 37% and 12%, respectively. Therefore, the seasonal two-factor RTMs are recommended for the research and applications of GNSS meteorology in the Hunan region, China.