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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

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Showing 2020Show all

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physics.ao-ph2020★ 22 cited

On temporal scale separation in coupled data assimilation with the ensemble Kalman filter

Maxime Tondeur, Alberto Carrassi, Stephane Vannitsem +1

Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward worldwide. The core issue is the…

stat.ML2020★ 99 cited

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…

stat.ML2020★ 206 cited

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…