143 citations · 304 across the 12 of their papers we have counts for
12 papers
Online machine-learning forecast uncertainty estimation for sequential data assimilation
Maximiliano A. Sacco, Manuel Pulido, Juan J. Ruiz +1
Quantifying forecast uncertainty is a key aspect of state-of-the-art numerical weather prediction and data assimilation systems. Ensemble-based data assimilation systems incorporat…
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
Maximiliano A. Sacco, Juan J. Ruiz, Manuel Pulido +1
Ensemble forecasting is, so far, the most successful approach to produce relevant forecasts with an estimation of their uncertainty. The main limitations of ensemble forecasting ar…
Inference in epidemiological agent-based models using ensemble-based data assimilation
Tadeo Javier Cocucci, Manuel Pulido, Juan Aparicio +3
To represent the complex individual interactions in the dynamics of disease spread informed by data, the coupling of an epidemiological agent-based model with the ensemble Kalman f…
A Framework for Causal Discovery in non-intervenable systems
Peter Jan van Leeuwen, Michael DeCaria, Nachiketa Chakaborty +1
Many frameworks exist to infer cause and effect relations in complex nonlinear systems but a complete theory is lacking. A new framework is presented that is fully nonlinear, provi…
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…
Stochastic parameterization identification using ensemble Kalman filtering combined with expectation-maximization and Newton-Raphson maximum likelihood methods
Manuel Pulido, Pierre Tandeo, Marc Bocquet +2
For modelling geophysical systems, large-scale processes are described through a set of coarse-grained dynamical equations while small-scale processes are represented via parameter…
DADA: Data Assimilation for the Detection and Attribution of Weather- and Climate-related Events
Alexis Hannart, Alberto Carrassi, Marc Bocquet +5
We describe a new approach allowing for systematic causal attribution of weather and climate-related events, in near-real time. The method is purposely designed to facilitate its i…