3 citations · 6 across the 3 of their papers we have counts for
7 papers
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…
Kernel embedded nonlinear observational mappings in the variational mapping particle filter
Manuel Pulido, Peter Jan vanLeeuwen, Derek J. Posselt
Recently, some works have suggested methods to combine variational probabilistic inference with Monte Carlo sampling. One promising approach is via local optimal transport. In this…
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…
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation
Pierre Tandeo, Pierre Ailliot, Marc Bocquet +4
Data assimilation combines forecasts from a numerical model with observations. Most of the current data assimilation algorithms consider the model and observation error terms as ad…
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter
Manuel Pulido, Peter Jan vanLeeuwen
In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles. Particles ar…