The Study On Optimization Of Mapping Function Models In Gnss Remote Sensing Ocean Water Vapor

Tropospheric mapping function is the mainly factor that influence the accuracy of location parameters, geophysical parameters, meteorological parameters. We always raise the elevation angle and remove the low elevation angle of GNSS observations to improve the accuracy of position and orbit in GNSS data processing, which reduces the utility of observation data. Only the coastal observation stations can make up for the lack of intensive and stable platforms on the ocean. In order to obtain water vapor information over the sea, we need to improve GNSS data processing method and use a low elevation angle of the satellite signal. Therefore, the accuracy of the mapping function should be improved.In order to improve the accuracy of low height angle mapping function, this paper optimizes the NMF model in ocean vapor remote sensing at low elevation angle to get a dynamic mapping function with better accuracy and suitable for low height angle. Refractive index field and Numerical meteorological mapping function are established with 3D meteorological data on the Bohai Bay. Taken the above as standard, common mapping functions are compared, such as NMF, VMF1 and GMF, etc. It is concluded that the meteorologic model based on the interpolation of 3D meteorological data, such as VMF1 and GMF, is much more accurate than other model. This paper calculates the GPS vapor information with Bernese5.0 and evaluates the GPS vapor results with respect to the consequence resulted from Numerical meteorological model. It can be concluded that time delays in the zenith differ no more than 2cm and fluctuate at 1cm. The wet delays differ at 7mm and maximize at 10mm. This paper discussed the impacts of Stations meteorological parameters, the uneven distribution of water vapor and satellite elevation angle on the NMF. With the rational analysis of factors that influence NMF, the practicability of the improvement of NMF is verified. And CNMF according to multiple linear regressions is established. It is concluded that CNMF, which is corrected from NMF and based on numerical weather models to improve the wet mapping function, considered the satellite elevation angle, station meteorological factors and the impact of the uneven distribution of water vapor. And it is applicable to ocean water vapor remote sensing technology and can improve accuracy of the low elevation angle mapping function.