Search Results for author: Julien Brajard

Found 8 papers, 1 papers with code

Super-resolution data assimilation

no code implementations4 Sep 2021 Sébastien Barthélémy, Julien Brajard, Laurent Bertino, François Counillon

Increasing the resolution of a model can improve the performance of a data assimilation system: first because model field are in better agreement with high resolution observations, then the corrections are better sustained and, with ensemble data assimilation, the forecast error covariances are improved.

Super-Resolution

Bridging observation, theory and numerical simulation of the ocean using Machine Learning

no code implementations26 Apr 2021 Maike Sonnewald, Redouane Lguensat, Daniel C. Jones, Peter D. Dueben, Julien Brajard, Venkatramani Balaji

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.

Time Series

Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting

1 code implementation9 Dec 2020 Vincent Bouget, Dominique Béréziat, Julien Brajard, Anastase Charantonis, Arthur Filoche

The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow.

Optical Flow Estimation

Combining data assimilation and machine learning to infer unresolved scale parametrisation

no code implementations9 Sep 2020 Julien Brajard, Alberto Carrassi, Marc Bocquet, Laurent Bertino

Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.

Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization

no code implementations17 Jan 2020 Marc Bocquet, Julien Brajard, Alberto Carrassi, Laurent Bertino

The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics.

Bayesian Inference Time Series

Representing ill-known parts of a numerical model using a machine learning approach

no code implementations18 Mar 2019 Julien Brajard, Anastase Charantonis, Jérôme Sirven

In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed.

Learning Dynamical Systems from Partial Observations

no code implementations26 Feb 2019 Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot, Julien Brajard, Patrick Gallinari

We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state.

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