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Algorithms 2009, 2(1), 31-45; doi:10.3390/a2010031

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

Dept. of Electronic and Information Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy
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Received: 9 December 2008 / Revised: 12 January 2009 / Accepted: 14 January 2009 / Published: 20 January 2009
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Abstract

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.
Keywords: Neural networks; GPS; radio occultations; tropospheric profiles Neural networks; GPS; radio occultations; tropospheric profiles
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Bonafoni, S.; Pelliccia, F.; Anniballe, R. Comparison of Different Neural Network Approaches for the Tropospheric Profiling over the Inter-tropical lands Using GPS Radio Occultation Data. Algorithms 2009, 2, 31-45.

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