A Fleet‑Scale Data-driven Framework for Global Wave Sensing (Fleet-GLOWS)

Accurate ocean-wave data are essential for weather forecasting, climate research, and safe, fuel-efficient shipping, yet conventional wave buoys provide sparse coverage. In principal, this project transforms the global merchant fleet into a distributed wave-observing system using ship motion measurements (MRU data) already collected onboard. Moving beyond single-vessel studies, we analyse MRU data from about 150 container ships in worldwide service and treat wave estimation as a fleet-scale inference problem. By combining hydrodynamic transfer functions with physics-informed neural networks, transfer learning, and uncertainty quantification, the framework embeds physical constraints while leveraging large-scale data. Aggregating diverse vessel responses reduces bias and uncertainty without new hardware. The project establishes scalable, self-validating wave estimation with impact on maritime safety, operational efficiency, and improved ocean observations for weather and climate models.