Comparison of Different Neural Network Approaches for the Tropospheric Profiling over the Inter-tropical lands Using GPS Radio Occultation Data

Algorithms

Stefania Bonafoni¹  Fabrizio Pelliccia² 

[1] Dept. of Electronic and Information Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy, Ph. [phone], Fax [phone] E-mail

关键词: Neural networks;    GPS;    radio occultations;    tropospheric profiles;   

DOI  :  10.3390/a2010031

来源: mdpi

【 摘 要 】

In this study different approaches based on multilayer perceptron neural networks are proposed and evaluated with the aim to retrieve tropospheric profiles by using GPS radio occultation data. We employed a data set of 445 occultations covering the land surface within the Tropics, split into desert and vegetation zone. The neural networks were trained with refractivity profiles as input computed from geometrical occultation parameters provided by 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. Such a new retrieval algorithm was chosen to solve the atmospheric profiling problem without the constraint of an independent knowledge of one atmospheric parameter at each GPS occultation.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland.