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

Abstract

In this study, radiosonde observations during the period of 2012-2013 from three stations in the Hunan region, China, were used to establish regional Tm models (RTMs) that are a fitting function of multiple meteorological factors (Ts, Es, and Ps). One-factor, two-factor, and three-factor RTMs were assessed by comparing their Tm against the radiosonde-derived Tm (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.