In this work a method based on neural networks is proposed to retrieve profiles of refractivity, temperature, pressure and humidity in the troposphere from GPS-LEO radio occultation. To overcome the constraint of temperature profile availability at each GPS occultation, we have trained neural networks with refractivity profiles as input obtained from COSMIC satellites, while the outputs are the refractivity component profiles and the dry pressure profiles obtained from the contemporary ECMWF data.
Sviluppo di reti neurali per la determinazione di parametri atmosferici da radio occultazione GPS-COSMIC
BONAFONI, Stefania;BASILI, Patrizia;
2008
Abstract
In this work a method based on neural networks is proposed to retrieve profiles of refractivity, temperature, pressure and humidity in the troposphere from GPS-LEO radio occultation. To overcome the constraint of temperature profile availability at each GPS occultation, we have trained neural networks with refractivity profiles as input obtained from COSMIC satellites, while the outputs are the refractivity component profiles and the dry pressure profiles obtained from the contemporary ECMWF data.File in questo prodotto:
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