## 1 Introduction  
Global Positioning System (GPS) radio occultation (RO) is a global sounding technique for the atmospheric profiling useful for numerical weather models and climate studies. The RO technique employs GPS receivers placed on Low-Earth Orbit (LEO) satellites to sound the Earth's atmosphere and ionosphere evaluating the additional delay affecting a radio signal when passing through the atmosphere due to the refractivity index variations [1].  
This limb-sounding technique works under all-weather conditions due to the insensitivity of the GPS signal wavelength to scattering by clouds, aerosols, and precipitation, with a vertical resolution of about 1 km but a poor horizontal resolution (about 200 km).  
Then, such GPS-LEO system is exploited to obtain profiles of refractivity, temperature, pressure and humidity in the atmosphere at global scale. Although the atmospheric refractivity profiling by radio occultation is a well-defined problem, care must be taken to compute temperature and particularly humidity profiles from refractivity [2]. In the troposphere, given an independent knowledge of temperature derived from independent observations (i.e. radiosoundings or data from atmospheric numerical modeling), GPS refractivity measurements are employed to derive water vapor partial pressure [3].  
In this paper, we have proposed a new retrieval algorithm based on multilayer perceptron neural networks to derive profile of atmospheric parameters from RO refractivity profiles overcoming the requirement for temperature profile availability at each GPS occultation. In particular, we have implemented and compared different neural network trainings, employing three neural networks at each approach. The inputs are refractivity profiles computed from the occultation parameters observed by the COSMIC Microsat Constellation satellites and provided by the COSMIC Data Analysis and Archive Center (CDAAC) of Boulder (Colorado) [4]. The targets employed in the training are the dry and wet refractivity profiles, together with the dry pressure ones, obtained from the contemporary European Centre for Medium-Range Weather Forecast (ECMWF) analysis data.  
The neural network training and the following independent test were performed over the entire land area between Tropics, split into a desert and vegetation zone, by using the available data set of 445 refractivity profiles on summer 2006, from July 17 to August 18. The three networks estimating wet and dry refractivity and dry pressure allow us to obtain temperature and water vapor pressure profiles, without requiring independent information on atmospheric temperature.  
The choice of splitting the entire available data set into desert and vegetation databases is to obtain three neural networks able to recognize homogeneous atmospheric conditions, since the humidity is affected by a large variability in the low troposphere, particularly.  
To evaluate the performances of the different approaches, we have computed errors affecting the estimated profiles with respect to ECMWF analysis, assumed as the truth. Such a choice of ECMWF data as reference in the comparisons was also adopted by other authors [5; 6], considering that these data provide global coverage and high spatial resolution reconstruction of the atmosphere.  
The proposed algorithm shows the possibility to estimate tropospheric parameters included the wet ones only from RO refractivity, after the settlement of the training phase of neural networks, and hence the possibility to increase the atmospheric observations, thanks to a wide spatial coverage of RO soundings. For this purpose, the employment of neural networks proved useful and hence different training approaches were tested and evaluated not only from the standpoint of the retrieval accuracy, but also in terms of computational cost such as time and memory requirements.