Communications in Mathematical Sciences

Volume 15 (2017)

Number 2

Derivation and analysis of simplified filters

Pages: 413 – 450



Wonjung Lee (Mathematics Institute and Centre for Predictive Modelling, School of Engineering, University of Warwick, United Kingdom; and Department of Mathematics, City University of Hong Kong)

Andrew Stuart (Mathematics Institute, University of Warwick, United Kingdom)


Filtering is concerned with the sequential estimation of the state, and uncertainties, of a Markovian system, given noisy observations. It is particularly difficult to achieve accurate filtering in complex dynamical systems, such as those arising in turbulence, in which effective low-dimensional representation of the desired probability distribution is challenging. Nonetheless recent advances have shown considerable success in filtering based on certain carefully chosen simplifications of the underlying system, which allow closed form filters. This leads to filtering algorithms with significant, but judiciously chosen, model error. The purpose of this article is to analyze the effectiveness of these simplified filters, and to suggest modifications of them which lead to improved filtering in certain time-scale regimes. We employ a Markov switching process for the true signal underlying the data, rather than working with a fully resolved DNS PDE model. Such Markov switching models haven been demonstrated to provide an excellent surrogate test-bed for the turbulent bursting phenomena which make filtering of complex physical models, such as those arising in atmospheric sciences, so challenging.


Bayesian statistics, sequential data assimilation, filtering with model error

2010 Mathematics Subject Classification

60G35, 93E11, 94A12

Published 21 February 2017