• Treffer 1 von 7
Zurück zur Trefferliste

Ensemble transform algorithms for nonlinear smoothing problems

  • Several numerical tools designed to overcome the challenges of smoothing in a non-linear and non-Gaussian setting are investigated for a class of particle smoothers. The considered family of smoothers is induced by the class of linear ensemble transform filters which contains classical filters such as the stochastic ensemble Kalman filter, the ensemble square root filter, and the recently introduced nonlinear ensemble transform filter. Further the ensemble transform particle smoother is introduced and particularly highlighted as it is consistent in the particle limit and does not require assumptions with respect to the family of the posterior distribution. The linear update pattern of the considered class of linear ensemble transform smoothers allows one to implement important supplementary techniques such as adaptive spread corrections, hybrid formulations, and localization in order to facilitate their application to complex estimation problems. These additional features are derived and numerically investigated for a sequence ofSeveral numerical tools designed to overcome the challenges of smoothing in a non-linear and non-Gaussian setting are investigated for a class of particle smoothers. The considered family of smoothers is induced by the class of linear ensemble transform filters which contains classical filters such as the stochastic ensemble Kalman filter, the ensemble square root filter, and the recently introduced nonlinear ensemble transform filter. Further the ensemble transform particle smoother is introduced and particularly highlighted as it is consistent in the particle limit and does not require assumptions with respect to the family of the posterior distribution. The linear update pattern of the considered class of linear ensemble transform smoothers allows one to implement important supplementary techniques such as adaptive spread corrections, hybrid formulations, and localization in order to facilitate their application to complex estimation problems. These additional features are derived and numerically investigated for a sequence of increasingly challenging test problems.zeige mehrzeige weniger

Metadaten exportieren

Weitere Dienste

Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Verfasserangaben:Jana de WiljesORCiDGND, Sahani Darschika PathirajaORCiD, Sebastian ReichORCiDGND
DOI:https://doi.org/10.1137/19M1239544
ISSN:1064-8275
ISSN:1095-7197
Titel des übergeordneten Werks (Englisch):SIAM journal on scientific computing
Verlag:Society for Industrial and Applied Mathematics
Verlagsort:Philadelphia
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:28.10.2019
Erscheinungsjahr:2020
Datum der Freischaltung:13.03.2023
Freies Schlagwort / Tag:adaptive; data assimilation; localization; optimal transport; smoother; spread correction
Band:42
Ausgabe:1
Seitenanzahl:28
Erste Seite:A87
Letzte Seite:A114
Fördernde Institution:Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG); [CRC 1294]
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
DDC-Klassifikation:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Peer Review:Referiert
Publikationsweg:Open Access / Green Open-Access
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.