The Model Forest Ensemble Kalman Filter

TL;DR

This paper introduces the model forest ensemble Kalman filter, a novel data assimilation method that integrates multiple models of varying fidelity organized in a forest structure, enhancing accuracy and efficiency.

Contribution

It generalizes the multifidelity ensemble Kalman filter to a model forest framework, enabling flexible integration of diverse models in data assimilation.

Findings

Validated with quasi-geostrophic models and reduced order models.

Demonstrated improved accuracy over traditional methods.

Showed increased flexibility between accuracy and computational speed.

Abstract

Traditional data assimilation uses information obtained from the propagation of one physics-driven model and combines it with information derived from real-world observations in order to obtain a better estimate of the truth of some natural process. However, in many situations multiple simulation models that describe the same physical phenomenon are available. Such models can have different sources. On one hand there are theory-guided models are constructed from first physical principles, while on the other there are data-driven models that are constructed from snapshots of high fidelity information. In this work we provide a possible way to make use of this collection of models in data assimilation by generalizing the idea of model hierarchies into model forests -- collections of high fidelity and low fidelity models organized in a groping of model trees such as to capture various relationships between different models. We generalize the multifidelity ensemble Kalman filter that previously operated on model hierarchies into the model forest ensemble Kalman filter through a generalized theory of linear control variates. This new filter allows for much more freedom when treading the line between accuracy and speed. Numerical experiments with a high fidelity quasi-geostrophic model and two of its low fidelity reduced order models validate the accuracy of our approach.