Unbiased ensemble square root filters

DOI10.1016/j.physd.2008.01.005zbMath1147.93413OpenAlexW1985356676MaRDI QIDQ928942

David M. Livings, Nancy K. Nichols, Sarah L. Dance

Publication date: 11 June 2008

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2008.01.005

zbMATH Keywords

data assimilationensemble

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11)

Related Items (17)

Mixture ensemble Kalman filters ⋮ Uncertainties in the 2004 Sumatra–Andaman source through nonlinear stochastic inversion of tsunami waves ⋮ Convergence acceleration of ensemble Kalman inversion in nonlinear settings ⋮ Iterated gain-based stochastic filters for dynamic system identification ⋮ On the mathematical theory of ensemble (linear-Gaussian) Kalman-Bucy filtering ⋮ Numerical linear algebra in data assimilation ⋮ Real-time thermoacoustic data assimilation ⋮ Tikhonov Regularization within Ensemble Kalman Inversion ⋮ A Kushner-Stratonovich Monte Carlo filter applied to nonlinear dynamical system identification ⋮ Combined state and parameter estimation in level-set methods ⋮ Unbiased ensemble square root filters ⋮ On exponential convergence of nonlinear gradient dynamics system with application to square root finding ⋮ Ensemble data assimilation in the presence of cloud ⋮ Ensemble Kalman filter with the unscented transform ⋮ Ensemble Kalman inversion for nonlinear problems: weights, consistency, and variance bounds ⋮ Mean field limit of ensemble square root filters -- discrete and continuous time ⋮ Score matching filters for Gaussian Markov random fields with a linear model of the precision matrix

Uses Software

• EnKF

Cites Work

• Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter vari\-ants
• Unbiased ensemble square root filters
• A family of embedded Runge-Kutta formulae
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• Stochastic processes and filtering theory
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