Abstract:
Observing the ocean’s kilometer-scale (i.e.,submesoscale) surface currents has been a long-standing challenge, with direct application to improving weather and climate models, marine navigation and a better understanding of ocean ecosystems. In this talk we review lessons learned measuring these currents during the NASA S-MODE campaign from both in situ assets such as wave gliders and doppler-based remote sensing techniques. The campaign highlighted that these spatially heterogeneous rapidly evolving submesoscale currents are particularly difficult to measure. To overcome this, we introduce Geostationary Ocean Flow (GOFLOW), a deep learning framework that produces hourly, high-resolution velocity fields from sequences of thermal imagery from geostationary satellites. Our approach directly measures the flow without assuming geostrophic balance and inherently filters internal wave noise that contaminates state-of-the-art satellite altimetry (e.g. the recently launched SWOT satellite). Applying GOFLOW to the Gulf Stream, we provide the first satellite-based measurements of submesoscale current statistics, revealing characteristic asymmetries in vorticity and divergence previously documented only in high-resolution circulation models. This ability to routinely map the ocean’s energetic submesoscale currents provides a new data source for advancing Earth system forecasting.
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https://umassd.zoom.us/j/97440069270
Meeting ID: 974 4006 9270
Passcode: 428029
