188 citations · 511 across the 15 of their papers we have counts for
15 papers
Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors
Maxime Beauchamp, Ronan Fablet, Simon Benaichouche +3
The spatio-temporal interpolation of large geophysical datasets has historically been addressed by Optimal Interpolation (OI) and more sophisticated equation-based or data-driven D…
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…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…
Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
Aurélien Colin, Pierre Tandeo, Charles Peureux +2
Synthetic Aperture Radar is known to be able to provide high-resolution estimates of surface wind speed. These estimates usually rely on a Geophysical Model Function (GMF) that has…
Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
Aurélien Colin, Pierre Tandeo, Charles Peureux +3
Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring…
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…
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…
Using local dynamics to explain analog forecasting of chaotic systems
P Platzer, P. Yiou, P. Naveau +4
Analogs are nearest neighbors of the state of a system. By using analogs and their successors in time, one is able to produce empirical forecasts. Several analog forecasting method…
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…
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
Redouane Lguensat, Miao Sun, Ronan Fablet +3
This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine…
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…