206 citations · 966 across the 31 of their papers we have counts for
7 papers · 1 filter
Data assimilation for chaotic dynamics
Alberto Carrassi, Marc Bocquet, Jonathan Demaeyer +3
Chaos is ubiquitous in physical systems. The associated sensitivity to initial conditions is a significant obstacle in forecasting the weather and other geophysical fluid flows. Da…
Ensemble Kalman Filter for non-conservative moving mesh solvers with a joint physics and mesh location update
Christian Sampson, Alberto Carrassi, Ali Aydoğdu +1
Numerical solvers using adaptive meshes can focus computational power on important regions of a model domain capturing important or unresolved physics. The adaptation can be inform…
Probabilistic forecasts of sea ice trajectories in the Arctic: impact of uncertainties in surface wind and ice cohesion
Sukun Cheng, Ali Aydoğdu, Pierre Rampal +2
We study the response of the Lagrangian sea ice model neXtSIM to the uncertainty in the sea surface wind and sea ice cohesion. The ice mechanics in neXtSIM is based on a brittle-li…
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…
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…
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…