Estimativa do vapor d’água integrado utilizando dados de estações GNSS terrestres para aplicações na troposfera sobre as cidades de Natal e Mossoró, no Estado do Rio Grande do Norte, Brasil

Abstract

The GNSS signal analysis (Global Navigation Satellite System) issued by satellites has been widely used in the field of geodynamics and geodesy, as a sensor for speed measurements and displacement of tectonic plates and the representation of the shape and the Earth's surface. However, the satellite signal is delayed as it crosses the earth's atmosphere, specifically in two of its layers: (a) the ionospheric layer, where the signal is delayed by ionized atoms present in this region, and (b) the tropospheric layer, due to the presence of water vapor, and is strongly related to the amount of water vapor precipitable present in that region. This work presents data analysis of GNSS signals obtained from surface gauge stations, aiming applications related to the amount of water vapor in the troposphere. Data from the GNSS signals were obtained directly from the IBGE (Instituto Brasileiro de Geografia e Estatística) through its link with RBMC (Rede Brasileira de Monitoramento Continuo dos Sistemas GNSS). Data processing was performed using the GIPSY (GPS Inferred Positioning System) software, from JPL-NASA (Jet Propulsion Laboratory), which processes the observed data from satellites and provides ZTD values (Zenithal Tropospheric Delay). From the knowledge of temperature and pressure in the gauge station antenna, one can estimate IWV (Integrated Water Vapor), that means the water vapor in the atmospheric column and is related to the local pluviometry. Applications has been made on Natal and Mossoró cities and were made correlations between the variables from the time series obtained from INMET (Instituto Nacional de Meteorologia), for the ZTD parameters, IWV and Pluviometry, using statistical analysis from the R-software. Statistical correlations between GNSS and Pluviometry data could be used as a tool for NWP (Numerical Weather Prediction). This work shows, without a doubt, that this happen when IWV is used as input data for Nowcasting applications.