Estimation of tropospheric profiles using COSMIC GPS radio occultation data with neural networks

In this work we have proposed a method based on neural networks to directly retrieve dry and wet refractivity and dry pressure profiles in troposphere (which in turn can be used to obtain temperature and humidity profiles) by using COSMIC GPS radio occultation (RO) data. To overcome the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation, we trained different neural networks with refractivity profiles as input, while the targets were the dry and wet refractivity profiles and the dry pressure profiles obtained from the ECMWF data. Finally, selecting two cases, we have compared the estimated profiles, for each neural network approach, with the corresponding radiosounding profiles.