206 citations · 966 across the 31 of their papers we have counts for
4 papers · 1 filter
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
Marc Bocquet, Alban Farchi, Tobias S. Finn +5
We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, fr…
Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF
A. Carrassi, A. Trevisan, L. Descamps +2
A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVar-AUS) is tested in a QG model, under perfect model conditions, with a fixed observa…
Model error and sequential data assimilation. A deterministic formulation
A. Carrassi, S. Vannitsem, C. Nicolis
Data assimilation schemes are confronted with the presence of model errors arising from the imperfect description of atmospheric dynamics. These errors are usually modeled on the b…
Data assimilation as a nonlinear dynamical systems problem: Stability and convergence of the prediction-assimilation system
Alberto Carrassi, Michael Ghil, Anna Trevisan +1
We study prediction-assimilation systems, which have become routine in meteorology and oceanography and are rapidly spreading to other areas of the geosciences and of continuum phy…