206 citations · 594 across the 12 of their papers we have counts for
12 papers
Towards diffusion models for large-scale sea-ice modelling
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi +2
We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by d…
Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks
Simon Driscoll, Alberto Carrassi, Julien Brajard +3
Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming climate change impacts, and more. A key feature in Arctic sea ice is the formation…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
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…
Super-resolution data assimilation
Sébastien Barthélémy, Julien Brajard, Laurent Bertino +1
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,…
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Maike Sonnewald, Redouane Lguensat, Daniel C. Jones +3
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques…
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-…
Learning Dynamical Systems from Partial Observations
Ibrahim Ayed, Emmanuel de Bézenac, Arthur Pajot +2
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state. We propose a natural dat…