Modelling of weighted-mean temperature using regional radiosonde observations in Hunan China

Abstract

Precipitable water vapour (PWV) over a ground station can be estimated from the global navigation satellite systems (GNSS) signal's zenith wet delays (ZWD) multiplying by a conversion factor that is a function of weighted-mean temperature (Tm). The commonly used Bevis Tm model (BTM) may not perform well in some regions due to its use of data from North America in the model development. In this study, radiosonde observations in 2012 from three stations - Changsha, Huaihua, Chenzhou in Hunan province, China - were used to establish a new regional Tm model (RTM) based on a numerical integration and the least squares estimation methods. Four seasonal RTMs were also established and assessed for 2012. The RTM-derived Tm at the three stations from 2012 - 2014 were validated by comparing it with radiosonde-derived Tm. Results showed that the accuracy of the yearly RTM was improved by 29% over the BTM, and the bias and root mean square (RMS) of all the four seasonal RTMs were slightly smaller than the yearly RTM, and the accuracy of spring, summer, autumn and winter Tm models is improved by 5, 13, 4, and 5% respectively. In addition, the bias and RMS of the differences between the GNSS-PWV resulting from the RTM-derived Tm and the radiosonde-PWV were 1.13 and 3.21 mm respectively, which are reduced by 34 and 10% respectively. Thus the seasonal RTMs are recommended for GNSS meteorology for Hunan Province.