Marginalized particle filtering framework for tuning of ensemble filters

Abstract

Marginalized particle filtering (MPF), also known as Rao-Blackwellized particle filtering, has been recently developed as a hybrid method combining analytical filters with particle filters. This paper investigates the prospects of this approach in environmental modeling where the key concerns are nonlinearity, highdimensionality, and computational cost. In the formulation herein, exact marginalization in the MPF is replaced by approximate marginalization, yielding a framework for creation of new hybrid filters. In particular, the authors propose to use the MPF framework for online tuning of nuisance parameters of ensemble filters. Conditional independence-based simplification of the MPF algorithm is proposed for computational reasons and its close relation to previously published methods is discussed. The 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. © 2011 American Meteorological Society.