Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
Quarterly Journal of the Royal Meteorological Society , volume=
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An MLP predicts the covariance difference between limited and large ensembles and applies an element-wise scaling correction to the EnKF forecast covariance, yielding higher analysis accuracy on Lorenz-63 and Lorenz-96 systems.
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Concentration Inequalities for Sample Cross-Covariances
Proves sharp operator-norm concentration and expectation bounds for sample cross-covariances of sub-Gaussian and Gaussian vectors, governed by effective ranks of the marginal covariances.
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Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction
An MLP predicts the covariance difference between limited and large ensembles and applies an element-wise scaling correction to the EnKF forecast covariance, yielding higher analysis accuracy on Lorenz-63 and Lorenz-96 systems.