17 citations · 32 across the 4 of their papers we have counts for
5 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…
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…
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…
Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem
Sammy Metref, Alexis Hannart, Juan Ruiz +3
IIn recent years, there has been a growing interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. In this se…
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…