Julien Brajard

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This paper presents a new development of the NeuroVaria method. NeuroVaria computes relevant atmospheric and oceanic parameters by minimizing the difference between the observed satellite reflectances and those computed from radiative transfer simulations modelled by artificial neural networks. Aerosol optical properties are computed using the Junge size(More)
This paper presents a statistical inversion method used to infer 3D data from 2D imaging. The methodology is based on a combination of the Self Organising Maps and the Hidden Markov Models. The SelfOrganising Maps generate the typical situations of the emissions and the hidden states of the Hidden Markov Model. The method has been validated by inferring the(More)
Variational data assimilation consists in estimating key control parameters of a numerical model in order to minimize the misfit between the model values and the actual observations. The YAO framework is a code generator based on a modular graph decomposition of the model; it is dedicated for helping data assimilation experiment achievement. YAO is(More)
The Senegalo-Mauritanian upwelling is a very productive upwelling occurring along the West coast of Africa. Its seasonal and inter-annual variability south of 20°N was analyzed by processing ocean color data and sea surface temperature provided by satellite sensors. We used a classification methodology consisting in a neural network topological map(More)
The present work addresses the problem of validation of a synthetic dataset with respect to observations. It gives an index that determines locally how much a region of the synthetic dataset fits the observations. The method uses an estimation of the probability density function computed with the probabilistic self-organizing maps. Then, an index F was(More)
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