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Ensemble Kalman Filter

The Ensemble Kalman Filter (EnKF) is a sequential Monte Carlo data assimilation algorithm introduced by Geir Evensen in 1994. It extends the classical Kalman filter to high-dimensional, nonlinear dynamical systems by representing the forecast error covariance through a finite ensemble of model realizations rather than propagating a full covariance matrix. Each ensemble member evolves through the nonlinear model, and observations are assimilated by computing a sample-based Kalman gain, making the method computationally tractable for large geophysical models.

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Ensemble Kalman Filter (Data Assimilation)

Registro del método taxonómico · regression-model / data-fusion

Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162. · DOI 10.1029/94JC00572

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Métodos relacionados

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Used in the same domainData Fusionmachine-suggested · Relational suggestion, not evidence.See alsoParticle Filtermachine-suggested · Relational suggestion, not evidence.Same method familyState Space Modelmachine-suggested · Relational suggestion, not evidence.