Abstract: The retrieval of temperature profiles in the atmosphere is a key issue in Meteorology, since it provides an important input for weather forecasting models, mainly in the Southern Hemisphere where there are large areas uncovered by data collecting ground stations. In this paper, vertical temperature profiles are derived from satellite data by using a Radial Basis Function neural network based inverse procedure. The Radial Basis Function is 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 from previous works. In addition to synthetic data, real radiation data from the HIRS/2 - High Resolution Infrared Radiation Sounder - is used as input for the neural network inverse procedure 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. and Ramos et al. [1, 2], 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 (2000): 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 (1999) – Proc. Book: pp. 235-238, Port Ludlow, Washington, USA, June 13-18, UEF-ASME.

[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.