Title :
A neural network approach for wind retrieval from the ERS-1 scatterometer data. 1. Determination of the geophysical model function of ERS-1 scatterometer
Author :
Mejia, C. ; Thiria, S. ; Crépon, M. ; Badran, F.
Author_Institution :
Lab. d´´Oceanogr. Dynamique et de Climatologie, Univ. Pierre et Marie Curie, Paris, France
Abstract :
Computes a new geophysical model function (GMF) for the ERS-1 scatterometer by the use of neural networks (NN). This NN-GMF is calibrated with ERS-1 scatterometer sigma0 collocated with ECMWF analysed wind vectors. In order to check the validity of the NN-GMF systematic comparisons with the ESA CMOD4-GMF (version 2) and the IFREMER CMOD2-I3-GMF are made. The GMF is used in many algorithms to retrieve the scatterometer wind
Keywords :
atmospheric techniques; meteorological radar; neural nets; remote sensing by radar; wind; ECMWF analysed wind vectors; ERS-1 scatterometer data; ESA CMOD4-GMF; IFREMER CMOD2-I3-GMF; geophysical model function; neural network approach; remote sensing; sigma0; wind retrieval; Computer networks; Geophysics computing; Information retrieval; Neural networks; Radar antennas; Radar cross section; Radar measurements; Radar scattering; Transfer functions; Wind speed;
Conference_Titel :
OCEANS '94. 'Oceans Engineering for Today's Technology and Tomorrow's Preservation.' Proceedings
Conference_Location :
Brest
Print_ISBN :
0-7803-2056-5
DOI :
10.1109/OCEANS.1994.363918
