Launching Drifter Observations in the Presence of Uncertainty
Determining the optimal locations for placing extra observational measurements has practical significance. However, the exact underlying flow field is never known in practice. Significant uncertainty appears when the flow field is inferred from a limited number of existing observations via data assi...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
24.07.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Determining the optimal locations for placing extra observational
measurements has practical significance. However, the exact underlying flow
field is never known in practice. Significant uncertainty appears when the flow
field is inferred from a limited number of existing observations via data
assimilation or statistical forecast. In this paper, a new computationally
efficient strategy for deploying Lagrangian drifters that highlights the
central role of uncertainty is developed. A nonlinear trajectory diagnostic
approach that underlines the importance of uncertainty is built to construct a
phase portrait map. It consists of both the geometric structure of the
underlying flow field and the uncertainty in the estimated state from
Lagrangian data assimilation. The drifters are deployed at the maxima of this
map and are required to be separated enough. Such a strategy allows the
drifters to travel the longest distances to collect both the local and global
information of the flow field. It also facilitates the reduction of a
significant amount of uncertainty. To characterize the uncertainty, the
estimated state is given by a probability density function (PDF). An
information metric is then introduced to assess the information gain in such a
PDF, which is fundamentally different from the traditional path-wise
measurements. The information metric also avoids using the unknown truth to
quantify the uncertainty reduction, making the method practical. Mathematical
analysis exploiting simple illustrative examples is used to validate the
strategy. Numerical simulations based on multiscale turbulent flows are then
adopted to demonstrate the advantages of this strategy over some other methods. |
---|---|
DOI: | 10.48550/arxiv.2307.12779 |