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05/12/2023
Online machine-learning forecast uncertainty estimation for sequential data assimilation
Quantifying forecast uncertainty is a key aspect of state-of-the-art num...
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11/29/2021
Evaluation of Machine Learning Techniques for Forecast Uncertainty Quantification
Producing an accurate weather forecast and a reliable quantification of ...
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10/29/2021
Inference in epidemiological agent-based models using ensemble-based data assimilation
To represent the complex individual interactions in the dynamics of dise...
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10/05/2020
A new Framework for Causal Discovery
Many frameworks exist to infer cause and effect relations in complex non...
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03/04/2020
Model error covariance estimation in particle and ensemble Kalman filters using an online expectation-maximization algorithm
The performance of ensemble-based data assimilation techniques that esti...
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11/04/2019
Model uncertainty estimation using the expectation maximization algorithm and a particle flow filter
Model error covariances play a central role in the performance of data a...
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01/29/2019
Kernel embedded nonlinear observational mappings in the variational mapping particle filter
Recently, some works have suggested methods to combine variational proba...
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07/30/2018
Joint Estimation of Model and Observation Error Covariance Matrices in Data Assimilation: a Review
This paper is a review of a crucial topic in data assimilation: the join...
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07/27/2018
Inference of stochastic parameterizations for model error treatment using nested ensemble Kalman filters
Stochastic parameterizations are increasingly being used to represent th...
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05/29/2018