Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

0367533 - ÚTIA 2012 RIV US eng J - Článek v odborném periodiku
Šmídl, Václav - Hofman, Radek
Marginalized Particle Filtering Framework for Tuning of Ensemble Filters.
Monthly Weather Review. Roč. 139, č. 11 (2011), s. 3589-3599. ISSN 0027-0644. E-ISSN 1520-0493
Grant CEP: GA MV VG20102013018; GA ČR GP102/08/P250
Výzkumný záměr: CEZ:AV0Z10750506
Klíčová slova: ensemble finter * marginalized particle filter * data assimilation
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
Impakt faktor: 2.688, rok: 2011
http://library.utia.cas.cz/separaty/2011/AS/smidl-0367533.pdf

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 improvements.
Trvalý link: http://hdl.handle.net/11104/0202179