Atmospheric water vapor (AWV) was investigated for the first time in the Chinese Bohai Sea using a Global Positioning System (GPS) receiver aboard a lightweight (300-ton) ship. An experiment was conducted to retrieve the AWV using the state-of-the-art GPS precise point positioning (PPP) technique. The effects of atmospheric weighted mean temperature model and zenith wet delay constraint on GPS AWV estimates were discussed in the PPP estimation system. The GPS-derived precipitable water vapor (PWV) and slant-path water vapor (SWV) were assessed by comparing with those derived from the Fifth Generation NCAR/Penn State Mesoscale Model (MM5). The results showed the PWV and SWV differences between those derived from both GPS and MM5 are 1.5 mm root mean square (RMS) with a bias of 0.2 and 3.9 mm RMS with a bias of -0.7 mm respectively. These good agreements indicate that the GPS-derived AWV in dynamic environments has a comparable accuracy with that of the MM5 model. This suggests that high accuracy and high spatio-temporal resolution humidity fields can be obtained using GPS in the Chinese Bohai Sea, which offers significant potential for meteorological applications and climate studies in this region.
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 (T m). The commonly used Bevis T m 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 T m 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 T m at the three stations from 2012-2014 were validated by comparing it with radiosondederived T m. 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 T m 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 T m 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.
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