Atmospheric Profiling in the Inter-Tropical Ocean Area Based on Neural Network Approach Using GPS Radio Occultations

Abstract

In this study we have proposed a method based on neural networks to retrieve refractivity, temperature, pressure and humidity profiles by using FORMOSAT-3/COSMIC GPS radio occultation data. To overcome the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation, we trained three neural networks with refractivity profiles as input computed from the geometrical occultation parameters relative to the FORMOSAT- 3/COSMIC satellites, while the targets were the dry and wet refractivity profiles and the dry pressure profiles obtained from the contemporary European Centre for Medium-Range Weather Forecast data. We have considered 1041 available satellite radio occultations covering the entire ocean area spanning within the Tropics during July-August 2006. We have used 937 profiles for training the neural networks, the remaining 104 ones for the independent test.