Avaliação do modelo de pressão e temperatura global da Universidade de Viena com dados de sensores meteorológicos no Brasil

GNSS signals are refracted when propagating through the neutral atmosphere, introducing the tropospheric delay, that can reach about 25 m, in the satellite-receiver distances. Due to the properties of the atmosphere, it is interesting to divide the tropospheric delay into 2 components: hydrostatic and wet. Models which require information on pressure, temperature and relative humidity of the air, as the one developed by Hopfield, can be used to mitigate the neutral atmosphere effects on GNSS signals, mainly of hydrostatic component. The meteorological data can be obtained from meteorological sensors, numerical weather predictions models, standard atmosphere reduction or empirical models, as the global pressure and temperature (GPT) model developed at University of Viena. Among the options cited, the empirical model can be highlighted by the facility of implementation. This research presents a comparison between the meteorological data computed by the GPT model and data from ten automatic meteorological stations in different regions of Brazil. Data from 2010 were used in the experiment. Analysis on pressure differences and temperature differences show that GPT computes such meteorological data with, respectively, 4,3 hPa and 4,5 °C of accuracy.