GRASE Real-Time Sea Experiment 2025

The objective of our optimal path planning is to maximize information about the Loop Current (LC) dynamics, especially the separation of LC eddies.

The platforms (e.g., gliders, floats, etc.) sample data (e.g., temperature and salinity profiles) along their tracks. They define the candidate sample observations. Commonly, a path is planned for a future time or date, and sampling region, within all operational constraints. The goal is to answer where, what and when to sample. For our information-theoretic objective function, optimal observations maximize the predicted information contained in the sampled data (the candidate observations) about the LC and its associated eddies (the target fields or verification fields). These candidate observations, target fields, and the objective function are defined as follows:

• The candidate observations are measurements made by the platform along its feasible track, in accord with all operational constraints and planned feasible observation times or data times.
• The target or verification fields are defined by their time(s), region(s), and field variable(s).
  • The target times can be a single time, an integral over time, or any vector of discrete times. In our case, we select a set of future times (e.g. same day, another later day, or every day thereafter) and evaluate the mutual information content for each of these times.
  • The target region is a grid covering the LC and the associated eddies, including hotspot regions of interest.
  • The target field variables are over the target region.
• The objective function optimized is the mutual information (MI) between the candidate observations and the target fields.
  • Note that if candidate observations are to be made at a single physical point or if the target variables are defined at a single spatiotemporal location, the MI is a scalar field defined over the verification region or candidate observation region, respectively.
  • In some sense, MI generalizes covariances to vector variables and non-Gaussian variables.

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