E.H. Shiguemiri, J.D.S. da Silva, H.F. de Campos Velho, J.C. Carvalho (2004): Neural Network based Models in the Inversion of Temperature Vertical Profiles from Satellite Data, Inverse Problems, Design and Optimization Symposium (IPDO), 17-19 March, Rio de Janeiro (RJ), Brazil.

Abstract: In this paper, vertical temperature profiles are inferred by a neural network based inverse procedure from satellite data. Multilayer Perceptrons and RBF networks are trained using data provided by the direct model characterized by the Radiative Transfer Equation (RTE). The neural network results are compared to the ones obtained by Carvalho et al. (1999) and Ramos et al. (1999), who used Tikhonov and maximum entropy principle regularization techniques. In addition, real radiation data from the HIRS/2 - High Resolution Infrared Radiation Sounder - is used as input for the neural networks to generate temperature profiles that are compared to measured temperature profiles from radiosonde. Analysis of the neural network results reveals the generated profiles closely approximate the results of Carvalho et al. (1999), thus showing adequacy of neural network based models in solving the inverse problem of temperature retrieval from satellite data. The advantages of using neural network based systems are related to their intrinsic features of parallelism and hardware implementation possibilities that may imply in very fast processing systems.

References

[1] J.C. Carvalho, F.M. Ramos, N.J. Ferreira H.F. de Campos Velho (1999): Retrieval of Vertical Temperature Profiles in the Atmosphere, 3rd International Conference on Inverse Problems in Engineering (3ICIPE), Proceedings in CD-ROM, under paper code HT02 (see also in the internet: http://www.me.ua.edu/3icipe/fin3prog.htm) - Proc. Book: pp. 235-238, Port Ludlow, Washington, USA, June 13-18, UEF-ASME (2000).

[2] F.M. Ramos, H.F. de Campos Velho, J.C. Carvalho, N.J. Ferreira (1999): Novel Approaches on Entropic Regularization, Inverse Problems, 15(5), 1139-1148.