|
 
|
[RBM00] Neural Network Wind Retrieval from ERS-1 Scatterometer Data
Revue Internationale avec comité de lecture : Journal Journal of geophysical Research, vol. 105, 2000
motcle:
Résumé:
This paper presents a neural network methodology to retrieve wind
vectors from ERS1 scatterometer data. First a neural network
(NN-INVERSE) computes the most probable wind vectors. Probabilities for
the estimated wind direction are given. At least 75 % of the most
probable wind directions are consistent with ECMWF winds (at ± 20°).
Then the remaining ambiguities are resolved by an adapted PRESCAT method
that uses the probabilities provided by NN-INVERSE. Several statistical
tests are presented to evaluate the skill of the method. The good
performance is mainly due to the use of a spatial context and to the
probabilistic approach adopted to estimate the wind direction.
Comparisons with other methods are also presented. The good performance
of the neural network method suggests that a self-consistent wind
retrieval from ERS1 Scatterometer is possible.
Commentaires:
pp 8737-8751, April 15, 2000
Collaboration:
UPMC
BibTeX
| @article { | |||
| RBM00, | |||
| title | = | "{Neural Network Wind Retrieval from ERS-1 Scatterometer Data}", | |
| author | = | "P. Richaume and F. Badran and C. Mejia and C. Crepon and H. Roquet and S. Thiria", | |
| journal | = | "Journal of geophysical Research", | |
| year | = | 2000, | |
| volume | = | 105, | |
| note | = | "{pp 8737-8751, April 15, 2000}", | |
| } | |||
