Issue 132 - May 2026

New Satellite-Based AI Approach Reveals Ocean Currents in Unprecedented Detail

A study published in Nature Geoscience introduces GOFLOW, an AI-powered method that turns existing weather satellites into a high-resolution lens on ocean surface currents, with implications for climate models, search and rescue, and oil spill response.

KINGSTON, R.I., April 20, 2026. A new study published in Nature Geoscience describes an artificial intelligence-powered technique that can measure ocean surface currents over broad areas in greater detail than ever before. Among the co-authors is Nick Pizzo of the University of Rhode Island Graduate School of Oceanography.

Called GOFLOW, short for Geostationary Ocean Flow, the approach uses AI to analyze thermal images from weather satellites already in orbit. Because it relies on existing satellites, no new hardware is required, marking what researchers describe as a major advancement in ocean observation.

GOFLOW AI-derived ocean surface temperature gradient in the Atlantic, showing fine-scale current detail — *GOFLOW temperature gradient computed in the Gulf Stream region of the Atlantic Ocean. Credit: Luc Lenain, Scripps Institution of Oceanography.*

The study was co-led by Luc Lenain of the Scripps Institution of Oceanography at the University of California San Diego and Kaushik Srinivasan of the University of California, Los Angeles. Co-author Roy Barkan of Tel Aviv University and Pizzo are also alumni of Scripps. The project was supported by grants from the Office of Naval Research, NASA, and the European Research Council.

Ocean currents and vertical mixing

Ocean currents play a huge role in shaping Earth’s weather and climate, transporting heat around the planet, moving carbon between the atmosphere and ocean interior, and carrying nutrients that support marine life.

“In areas where the ocean pushes together and pulls apart, information from the atmosphere and ocean interior are exchanged in ways we do not fully understand. This is one of the most exciting areas of physical oceanography today.”
Nick Pizzo, URI Graduate School of Oceanography

Understanding currents also matters for search-and-rescue efforts and for tracking the movement of oil spills. Yet measuring currents across large stretches of ocean has remained extremely difficult. Some satellites only revisit the same location about every 10 days, too infrequently to capture currents that can appear and disappear within hours. Ships and coastal radar can track faster changes, but only in limited areas.

This has left a persistent gap in observations at the scales where most of the ocean’s vertical mixing occurs, when shallower waters are mixed deeper, or vice versa. The phenomena that drive vertical mixing can be less than 10 kilometers (six miles) wide and transform in hours. Vertical mixing matters because it powers key processes such as bringing nutrients up to the surface and pumping carbon dioxide into the deep ocean, where it is stored long-term.

Deep learning, applied to a moving ocean

The GOFLOW team trained an AI model to recognize how surface temperature patterns shift as water moves below. The neural network learned from advanced computer simulations of ocean circulation, then applied that knowledge to real satellite imagery from the North Atlantic collected by the GOES-East weather satellite. The researchers tested the method against shipboard observations in the Gulf Stream and found that GOFLOW matched existing measurement techniques while revealing much finer detail, capturing smaller, more energetic features linked to vertical mixing.

Side-by-side comparison of ocean surface velocity and vorticity, GOFLOW versus AVISO, in the Gulf Stream — Side-by-side comparison of ocean surface velocity and vorticity fields. The GOFLOW map (a) is built from hourly data and reveals greater detail than the 10-day-averaged AVISO map (b). Credit: Luc Lenain, Scripps Institution of Oceanography.

For scientists such as Pizzo, these advances open new opportunities to study ocean dynamics through actual observations rather than relying primarily on computer models.

Portrait of Nick Pizzo, University of Rhode Island assistant professor of oceanography and GOFLOW co-author — *Nick Pizzo, URI assistant professor of physical oceanography and co-author of the GOFLOW study. Credit: URI Photo.*

“We are using this real-world inference to better understand how the ocean transports important quantities like heat from one place to another, and how vertical motions that are important for exchanges between the atmosphere and the ocean are supported.”
Nick Pizzo, URI Graduate School of Oceanography

Because GOFLOW works with satellites already in service, the method could eventually be integrated into weather forecasts and climate models, helping improve predictions of ocean-atmosphere interactions, marine debris transport, and ecosystem change. The researchers are now working to expand the method globally and to improve performance when cloud cover blocks satellite views.

This story was written by Mackensie duPont Crowley, digital communications coordinator in URI’s Graduate School of Oceanography, and is published with permission via the URI Communications and Marketing office.