On the convergence of a non-linear ensemble Kalman smoother

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00498774" target="_blank" >RIV/67985807:_____/19:00498774 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.apnum.2018.11.008" target="_blank" >http://dx.doi.org/10.1016/j.apnum.2018.11.008</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.apnum.2018.11.008" target="_blank" >10.1016/j.apnum.2018.11.008</a>

Result language
angličtina
Original language name
On the convergence of a non-linear ensemble Kalman smoother
Original language description
Ensemble methods, such as the ensemble Kalman filter (EnKF), the local ensemble transform Kalman filter (LETKF), and the ensemble Kalman smoother (EnKS) are widely used in sequential data assimilation, where state vectors are of huge dimension. Little is known, however, about the asymptotic behavior of ensemble methods. In this paper, we prove convergence in Lp of ensemble Kalman smoother to the Kalman smoother in the large-ensemble limit, as well as the convergence of EnKS-4DVAR, which is a Levenberg–Marquardt-like algorithm with EnKS as the linear solver, to the classical Levenberg–Marquardt algorithm in which the linearized problem is solved exactly.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GA13-34856S" target="_blank" >GA13-34856S: Advanced random field methods in data assimilation for short-term weather prediction</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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