# [MBB99] Determination of the Geophysical Model Function of NSCAT Scatterometer and its corresponding variance by the use of Neural Network

**Revue Internationale avec comité de lecture : **
*Journal Journal of Geophysical Research*,

vol. 104, 1999

**motcle: **

**Résumé: **
We have computed two Geophysical Model Functions (one for the vertical
and one for the horizontal polarization) for the NSCAT scatterometer by
using neural networks. These Neural Network Geophysical Model Functions
(NN-GMF) were estimated with NSCAT scatterometer sigma-0 measurements
collocated with ECMWF analyzed wind vectors during the period 15 January
1997 to 15 April 1997. We performed a Student t-test showing that the
NN-GMFs estimate the NSCAT sigma-0 with a confidence level of 95%.
Analysis of the results shows that the mean NSCAT signal depends on the
incidence angle, on the wind speed and presents the classical
bi-harmonic modulation with respect to the wind azimuth. The NSCAT
sigma-0 increases with respect to the wind speed and presents a well
marked change at around 7 m/s. The upwind-downwind amplitude is higher
for horizontal polarization signal than for vertical polarization
indicating that the use of horizontal polarization can give additional
information for wind retrieval. Comparison of the sigma-0 computed by
the NN-GMFs against the NSCAT measured sigma-0 show a quite low RMS
except at low wind speeds. We also computed two specific neural networks
for estimating the variance associated to these GMFs. The variances are
analyzed with respect to geophysical parameters. This lead us to compute
the geophysical signal to noise ratio, i.e. Kp. The Kp values are quite
high at low wind speed and decreases at high wind speed. At constant
wind speed, the highest Kp are at cross-wind directions showing that the
cross wind values are the most difficult to estimate. These neural
networks can be expressed as analytical functions and Fortran
subroutines can be provided.

**Commentaires: **
pp 11,539-11,556

Collaboration:
UPMC