Search Results for author: Pierre Tandeo

Found 9 papers, 2 papers with code

SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes

no code implementations2 Feb 2024 Maxime Beauchamp, Nicolas Desassis, J. Emmanuel Johnson, Simon Benaichouche, Pierre Tandeo, Ronan Fablet

Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework.

Gaussian Processes Uncertainty Quantification

Online machine-learning forecast uncertainty estimation for sequential data assimilation

no code implementations12 May 2023 Maximiliano A. Sacco, Manuel Pulido, Juan J. Ruiz, Pierre Tandeo

The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman-like analysis update and the machine learning based estimation of a state-dependent forecast error covariance matrix.

Uncertainty Quantification

Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks

no code implementations16 Mar 2023 Aurélien Colin, Pierre Tandeo, Charles Peureux, Romain Husson, Ronan Fablet

By carefully building a large dataset of SAR observations from the Copernicus Sentinel-1 mission, collocated with both GMF and atmospheric model wind speeds as well as rainfall estimates, we were able to train a wind speed estimator with reduced errors under rain.

Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification

no code implementations29 Nov 2021 Maximiliano A. Sacco, Juan J. Ruiz, Manuel Pulido, Pierre Tandeo

Experiments using the Lorenz'96 model show that the ANNs are able to emulate some of the properties of ensemble forecasts like the filtering of the most unpredictable modes and a state-dependent quantification of the forecast uncertainty.

BIG-bench Machine Learning Uncertainty Quantification

Probability distributions for analog-to-target distances

no code implementations26 Jan 2021 Paul Platzer, Pascal Yiou, Philippe Naveau, Jean-François Filipot, Maxime Thiebaut, Pierre Tandeo

These findings are illustrated with numerical simulations of a well-known chaotic dynamical system and on 10m-wind reanalysis data in north-west France.

Dimensionality Reduction

Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics

1 code implementation18 Oct 2019 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.

Time Series Time Series Analysis

EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

1 code implementation10 Nov 2017 Redouane Lguensat, Miao Sun, Ronan Fablet, Evan Mason, Pierre Tandeo, Ge Chen

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 and Environment Monitoring Service (CMEMS).

Decoder General Classification +1

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