output
20062024
most citedCombining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model

206 citations

researchers with a paper here
collaborating institutions

28 papers

cs.LG2024★ 1 cited

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…

cs.LG2022

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…

stat.AP2021★ 15 cited

Exploitation of error correlation in a large analysis validation: GlobCurrent case study

Richard E. Danielson, Johnny A. Johannessen, Graham D. Quartly +4

An assessment of variance in ocean current signal and noise shared by in situ observations (drifters) and a large gridded analysis (GlobCurrent) is sought as a function of day of t…

physics.ao-ph2021★ 44 cited

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,…

physics.ao-ph2021★ 30 cited

Reconciling high resolution climate datasets using KrigR

Richard Davy, Erik Kusch

There is an increasing need for high spatial and temporal resolution climate data for the wide community of researchers interested in climate change and its consequences. Currently…

physics.ao-ph2021★ 67 cited

KrigR -- A tool for downloading and statistically downscaling climate reanalysis data

Erik Kusch, Richard Davy

Advances in climate science have rendered obsolete gridded observation data sets commonly used in macroecological analyses. Novel climate reanalysis products outperform legacy data…