Decreasing flow uncertainty in Bayesian inverse problems through Lagrangian drifter control

TL;DR

This paper proposes a control strategy for ocean drifters to actively explore uncharted flow regions, reducing uncertainty in flow estimation by guiding drifters to navigate features like eddies, thereby improving flow models.

Contribution

It introduces a control method for ocean drifters that systematically reduces flow uncertainty by targeting uncharted regions, enhancing Bayesian inverse problem solutions.

Findings

Systematic variance reduction achieved through controlled drifter navigation.

Controls effectively guide drifters to explore eddies and other flow features.

Improved flow estimates with fewer uncertainties.

Abstract

Commonplace in oceanography is the collection of ocean drifter positions. Ocean drifters are devices that sit on the surface of the ocean and move with the flow, transmitting their position via GPS to stations on land. Using drifter data, it is possible to obtain a posterior on the underlying flow. This problem, however, is highly underdetermined. Through controlling an ocean drifter, we attempt to improve our knowledge of the underlying flow. We do this by instructing the drifter to explore parts of the flow currently uncharted, thereby obtaining fresh observations. The efficacy of a control is determined by its effect on the variance of the posterior distribution. A smaller variance is interpreted as a better understanding of the flow. We show a systematic reduction in variance can be achieved by utilising controls that allow the drifter to navigate new or interesting flow structures, a good example of which are eddies.