Between the clouds: An unprecedented view of ocean surface currents
Abstract
A major outstanding problem in physical oceanography is resolving high resolution spatio-temporal information of the ocean currentsand scalars like temperature. Currents facilitate the flux of mass, momentum and energy, strongly modulating Earth#s weather and climate and the transport of marine debris. The rapid spatio-temporal evolution of the physical properties of the upper ocean (e.g., temperature, salinity, density) expected in regions with strong submesoscale currents can also lead to complex acoustic propagation effects which need to be accounted for during ASW operations, especially as such effects could be aversely exploited.Traditional approaches are limited by point measurements (e.g. a ship) or snapshots in time (e.g. traditional satellites). Geostationary satellites offer high resolution broadband spatial measurements of the thermal structure of the surface of the ocean, though obscured by clouds. Until now, these satellites have not been used to derive information aboutsurface currents. Here, we propose to apply state of theart and novel machine learning techniques to remove clouds from GOES SST images and derive the surface velocity field from ocean basin scales to the submesoscale with sub-hour temporal resolution. These results will substantially improve the predictability and understanding of global ocean surface currents.This abstract is publicly releasable.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 12, 2025
- Source ID
- N000142512197
Entities
People
- Luc Lenain
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego