2 citations · 2 across the 3 of their papers we have counts for
6 papers
Learning 4DVAR inversion directly from observations
Arthur Filoche, Julien Brajard, Anastase Charantonis +1
Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive b…
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
Vincent Bouget, Dominique Béréziat, Julien Brajard +2
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven appro…
Combining data assimilation and machine learning to infer unresolved scale parametrisation
Julien Brajard, Alberto Carrassi, Marc Bocquet +1
In recent years, machine learning (ML) has been proposed to devise data-driven parametrisations of unresolved processes in dynamical numerical models. In most cases, the ML trainin…
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
Marc Bocquet, Julien Brajard, Alberto Carrassi +1
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…
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
Julien Brajard, Alberto Carassi, Marc Bocquet +1
A novel method, based on the combination of data assimilation and machine learning is introduced. The new hybrid approach is designed for a two-fold scope: (i) emulating hidden, po…
Representing ill-known parts of a numerical model using a machine learning approach
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-…