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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 2019Show all

3 papers · 1 filter

stat.AP2019★ 4 cited

Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ

Abhishek Shah, Laurent Bertino, Francois Counillon +2

A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the T…

eess.SY2019★ 50 cited

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…

physics.data-an2019★ 12 cited

Revising the stochastic iterative ensemble smoother

Patrick N. Raanes, Geir Evensen, Andreas S. Stordal

Ensemble randomized maximum likelihood (EnRML) is an iterative (stochastic) ensemble smoother, used for large and nonlinear inverse problems, such as history matching and data assi…