Atmospheric temperature retrieval using a Radial Basis Function Neural Network

  • Authors:

  • E. H. Shiguemori;J.D.S. Da Silva;H.F. De Campos Velho;J. C. Carvalho

  • Affiliations:

  • Lab. Associado de Computacao e Matematica Aplicada LAC, Instituto Nacional de Pesquisas Espaciais INPE, Sao Jose dos Campos, SP, Brazil/ Instituto de Estudos Avancados, Sao Jose dos Campos, SP, Br ...;Laboratorio Associado de Computacao e Matematica Aplicada LAC, Instituto Nacional de Pesquisas Espaciais INPE, Sao Jose dos Campos, SP, Brazil.;Laboratorio Associado de Computacao e Matematica Aplicada LAC, Instituto Nacional de Pesquisas Espaciais INPE, Sao Jose dos Campos, SP, Brazil.;Superintendencia de Administracao da Rede Hidrometeorologica SAR, Agencia Nacional de Aguas ANA, Brasilia, DF, Brazil

  • Venue:
  • International Journal of Information and Communication Technology
  • Year:
  • 2008

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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.