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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.
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.
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