Performance of Ensemble Kalman Filters in Large Dimensions

Contemporary data assimilation often involves more than a million prediction variables. Ensemble Kalman filters (EnKF) have been developed by geoscientists. They are successful indispensable tools in science and engineering, because they allow for computationally cheap low‐ensemble‐state approximati...

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Veröffentlicht in:	Communications on pure and applied mathematics 2018-05, Vol.71 (5), p.892-937
Hauptverfasser:	Majda, Andrew J., Tong, Xin T.
Format:	Artikel
Sprache:	eng
Schlagworte:	Applications of mathematics Covariance Kalman filters Turbulence
Online-Zugang:	Volltext
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