Several hybrid neutral atmosphere delay models have been developed at the University of New Brunswick. In this paper we are presenting UNB3m_pack, a package with subroutines in FORTRAN and corresponding functions in MatLab which provides neutral atmospheric information estimated using the UNB3m model. The main goal of UNB3m is to provide reliable predicted neutral atmosphere delays for users of global navigation satellite systems (GNSS) and other transatmospheric radiometric techniques. Slant neutral atmosphere delays are the main output of the package, however, it can be used to estimate zenith delays, Niell mapping functions values, delay rates, mapping function rates, station pressure, temperature, relative humidity and the mean temperature of water vapor in the atmospheric column. The subroutines work using day of year, latitude, height and elevation angle as input values. The files of the package have a commented section at the beginning, explaining how the subroutines work and what the input and output parameters are. The subroutines are self-contained, i.e., they do not need any auxiliary files. The user has simply to add to his/her software one or more of the available files and call them in the appropriate way.

This work demonstrates that precise point positioning (PPP) can be used not only for positioning, but for a variety of other tasks, such as signal analysis. The fact that the observation model used for accurate error modeling has to take into consideration the several effects present in GPS signals, and that observations are undifferenced, makes PPP a powerful data analysis tool sensitive to a variety of parameters. The PPP application developed at the University of New Brunswick, which is called GAPS (GPS Analysis and Positioning Software), has been designed and built in order to take advantage of available precise products, resulting in a data analysis tool for determining parameters in addition to position, receiver clock error, and neutral atmosphere delay. These other estimated parameters include ionospheric delays, code biases, satellite clock errors, and code multipath among others. In all cases, the procedures were developed in order to be suitable for real-time as well as post-processing applications. One of the main accomplishments in the development described here is the use of very precise satellite products, coupled with a very complete observation error modeling to make possible a variety of analyses based on GPS data. In this paper, several procedures are described, their innovative aspects are pointed out, and their results are analyzed and compared with other sources.The procedures and software are readily adaptable for using data from other global navigation satellite systems.

A Neural Network model has been developed for estimating the total electron content (TEC) of the ionosphere. TEC is proportional to the delay suffered by electromagnetic signals crossing the ionosphere and is among the errors that impact GNSS (Global Navigation Satellite Systems) observations. Ionospheric delay is particularly a problem for single frequency receivers, which cannot eliminate the (first-order) ionospheric delay by combining observations at two frequencies. Single frequency users rely on applying corrections based on prediction models or on regional models formed based on actual data collected by a network of receivers. A regional model based on a neural network has been designed and tested using data sets collected by the Brazilian GPS Network (RMBC) covering periods of low and high solar activity. Analysis of the results indicates that the model is capable of recovering, on average, 85% of TEC values.K e y w o r d s : total electron content, ionosphere, regional ionospheric model, neural network