Transdimensional Data Assimilation for dynamic model selection problems

TL;DR

This paper introduces a novel data assimilation algorithm that combines particle filters with transdimensional MCMC to adaptively change model complexity during dynamic, variable-dimensional problems, improving flexibility and efficiency.

Contribution

It develops a transdimensional particle filter that dynamically adjusts model parameters, addressing limitations of fixed-model approaches in data assimilation.

Findings

Successfully applied to simple model examples

Enables on-the-fly model complexity adjustment

Potentially improves computational efficiency

Abstract

In this paper we combine the non-linear filtering capabilities of particle filters with the transdimensional inference of the reversible-jump Markov chain Monte Carlo method for a data assimilation methodology over dynamic problems with variable dimensionality. By using transdimensional MCMC steps for the rejuvenation of the particle filter, the algorithm could change the number of state space parameters on the fly and can be applied for transdimensional data assimilation purposes. Classic inversion methodologies use pre-defined models, and only changes the individual parameter values during interpretation. This is often not feasible when the optimal model parametrization is not known a priori or when the model resolution needs to change with time. The proposed transdimensional particle filter algorithm, combines the advantages of particle filters and the transdimensional MCMC methods, and provides an easily implementable data assimilation algorithm that could tackle such problems. The methodology could also improve the computational efficiency of particle filters as it could inherently optimize the model complexity in a data-driven way. We demonstrate the capabilities of the enhanced algorithm on two simple model examples.