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Found 9 papers, 2 papers with code
SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes
Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework.
Online machine-learning forecast uncertainty estimation for sequential data assimilation
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.
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks
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.
Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
SAR satellites deliver very high resolution observations with a global coverage.
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
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.
Probability distributions for analog-to-target distances
These findings are illustrated with numerical simulations of a well-known chaotic dynamical system and on 10m-wind reanalysis data in north-west France.
Coupling Oceanic Observation Systems to Study Mesoscale Ocean Dynamics
Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns.
EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies
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).
