12 citations · 18 across the 2 of their papers we have counts for
4 papers
A Stochastic Covariance Shrinkage Approach in Ensemble Transform Kalman Filtering
Andrey A Popov, Adrian Sandu, Elias D. Nino-Ruiz +1
The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number o…
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…
Accounting for model errors in iterative ensemble smoothers
Geir Evensen
In the strong-constraint formulation of the history-matching problem, we assume that all the model errors relate to a selection of uncertain model input parameters. One does not ac…
Data Assimilation in the Geosciences - An overview on methods, issues and perspectives
Alberto Carrassi, Marc Bocquet, Laurent Bertino +1
We commonly refer to state-estimation theory in geosciences as data assimilation. This term encompasses the entire sequence of operations that, starting from the observations of a…