143 citations · 304 across the 12 of their papers we have counts for
3 papers · 1 filter
Model error covariance estimation in particle and ensemble Kalman filters using an online expectation-maximization algorithm
Tadeo Javier Cocucci, Manuel Pulido, Magdalena Lucini +1
The performance of ensemble-based data assimilation techniques that estimate the state of a dynamical system from partial observations depends crucially on the prescribed uncertain…
Model uncertainty estimation using the expectation maximization algorithm and a particle flow filter
María Magdalena Lucini, Peter Jan van Leeuwen, Manuel Pulido
Model error covariances play a central role in the performance of data assimilation methods applied to nonlinear state-space models. However, these covariances are largely unknown…
Inference of stochastic parameterizations for model error treatment using nested ensemble Kalman filters
Guillermo Scheffler, Juan Ruiz, Manuel Pulido
Stochastic parameterizations are increasingly being used to represent the uncertainty associated with model errors in ensemble forecasting and data assimilation. One of the challen…