The full text of this article:

Atmospheric temperature retrieval using a Radial Basis Function Neural Network
by E.H. Shiguemori, J.D.S. Da Silva, H.F. De Campos Velho, J.C. Carvalho
International Journal of Information and Communication Technology (IJICT), Vol. 1, No. 2, 2008
Abstract: Vertical temperature profiles are obtained from measured satellite radiance data by using a Radial Basis Function Neural Network (RBF-NN). The RBF-NN is trained with data provided by the direct model, characterised by the Radiative Transfer Equation. The results are compared with regularisation-based inverse solutions. The approach is tested using satellite radiances, and the inversion temperature profile is compared with radiosonde temperature measurements. Analysis reveals that the generated profiles are closely approximate to previous results, showing the methodology adequacy. ANNs are useful because of the parallelism and implementation simplicity, turn hardware implementation possible, that may imply in on-board and real-time systems.

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