Data Visualization to Evaluate and Facilitate Targeted Data Acquisitions in Support of a Real-time Ocean Forecasting System

TL;DR

This paper presents a visualization-based evaluation toolset designed to improve targeted data acquisition and adaptive sampling in a real-time ocean forecasting system, enhancing operational decision-making.

Contribution

The paper introduces a modular, Python-based visualization software suite that integrates modeling and operational environments for real-time ocean data collection.

Findings

Verifies glider waypoint adherence and predicts next cycle positions.

Ensures the usefulness and feasibility of delivered waypoints.

Provides confidence levels for suggested paths.

Abstract

A robust evaluation toolset has been designed for Naval Research Laboratory's Real-Time Ocean Forecasting System RELO with the purpose of facilitating an adaptive sampling strategy and providing more educated guidance for routing underwater gliders. The major challenges are to integrate into the existing operational system and provide a bridge between the modeling and operative environments. Visualization is the selected approach, and the developed software is divided into 3 packages. The first package verifies that the glider is actually following the waypoints and predicts the position of the glider for the next cycle's instructions. The second package ensures that the delivered waypoints are both useful and feasible. The third package provides the confidence levels for the suggested path. This software's implementation is in Python for portability and modularity to allow for easy expansion of new visuals.