Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F11%3A00367533" target="_blank" >RIV/67985556:_____/11:00367533 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1175/2011MWR3586.1" target="_blank" >http://dx.doi.org/10.1175/2011MWR3586.1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1175/2011MWR3586.1" target="_blank" >10.1175/2011MWR3586.1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

Popis výsledku v původním jazyce

Marginalized particle ltering (MPF), also known as Rao-Blackwellized particle filtering has been recently developed as a hybrid method combining analytical lters with particle filters. In this paper, we investigate the prospects of this approach in enviromental modelling where the key concerns are nonlinearity, high-dimensionality, and computational cost. In our formulation, exact marginalization in the MPF is replaced by approximate marginalization yielding a framework for creation of new hybrid lters.In particular, we propose to use the MPF framework for on-line tuning of nuisance parameters of ensemble filters. Strength of the framework is demonstrated on the joint estimation of the inflation factor, the measurement error variance and the length-scale parameter of covariance localization. It is shown that accurate estimation can be achieved with a moderate number of particles. Moreover, this result was achieved with naively chosen proposal densities leaving space for further improv

Název v anglickém jazyce

Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

Popis výsledku anglicky

Marginalized particle ltering (MPF), also known as Rao-Blackwellized particle filtering has been recently developed as a hybrid method combining analytical lters with particle filters. In this paper, we investigate the prospects of this approach in enviromental modelling where the key concerns are nonlinearity, high-dimensionality, and computational cost. In our formulation, exact marginalization in the MPF is replaced by approximate marginalization yielding a framework for creation of new hybrid lters.In particular, we propose to use the MPF framework for on-line tuning of nuisance parameters of ensemble filters. Strength of the framework is demonstrated on the joint estimation of the inflation factor, the measurement error variance and the length-scale parameter of covariance localization. It is shown that accurate estimation can be achieved with a moderate number of particles. Moreover, this result was achieved with naively chosen proposal densities leaving space for further improv

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Monthly Weather Review

ISSN

0027-0644

e-ISSN

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Svazek periodika

139

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

3589-3599

Kód UT WoS článku

000296475700014

EID výsledku v databázi Scopus

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