2 citations · 3 across the 2 of their papers we have counts for
6 papers
Probability distributions for analog-to-target distances
Paul Platzer, Pascal Yiou, Philippe Naveau +3
Some properties of chaotic dynamical systems can be probed through features of recurrences, also called analogs. In practice, analogs are nearest neighbours of the state of a syste…
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…
Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics
Gautier Cosne, Guillaume Maze, Pierre Tandeo
Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature…
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…
An efficient particle-based method for maximum likelihood estimation in nonlinear state-space models
Thi Tuyet Trang Chau, Pierre Ailliot, Valérie Monbet +1
Data assimilation methods aim at estimating the state of a system by combining observations with a physical model. When sequential data assimilation is considered, the joint distri…
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…