Dynamic Likelihood Filter

TL;DR

The paper introduces a dynamic likelihood filter that leverages model physics to improve data assimilation in hyperbolic problems, outperforming traditional methods especially with sparse observations, while maintaining computational efficiency.

Contribution

It presents a novel Bayesian data assimilation scheme that dynamically updates likelihoods using model physics, enhancing performance with sparse observations in hyperbolic problems.

Findings

Outperforms the Kalman filter and model outcomes with sparse observations.

Maintains linear computational complexity relative to the number of observations.

Effective for large-scale, advection-dominated problems.

Abstract

A Bayesian data assimilation scheme is formulated for advection-dominated or hyperbolic evolutionary problems, and observations. The method is referred to as the dynamic likelihood filter because it exploits the model physics to dynamically update the likelihood with the aim of making better use of low uncertainty sparse observations. The filter is applied to a problem with linear dynamics and Gaussian statistics, and compared to the exact estimate, a model outcome, and the Kalman filter estimate. Its estimates are shown to be superior to the model outcomes and the Kalman estimate, when the observation system is sparse. The added computational expense of the method is linear in the number of observations and thus computationally efficient, suggesting that the method is practical even if the space dimensions of the physical problem are large.