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arXiv Statistics ML By Rebecca Gjini, Matthias Morzfeld, Oliver R. A. Dunbar, Tapio Schneider

The Ensemble Kalman Inversion Race

Read the original on arXiv Statistics ML →

The paper compares different Ensemble Kalman methods for calibrating climate model parameters by minimizing the misfit between modeled and observed climate statistics. It conducts systematic numerical experiments on Lorenz-type models, including neural network parameterizations, to evaluate computational efficiency and accuracy of each method. The study examines how prior information and dimensionality affect the cost of these methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

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