(eng): 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.