Deep learning turns weather satellite thermal imagery into hourly ocean current maps
What to know about Deep learning turns weather satellite thermal imagery into hourly ocean current maps
Researchers from Scripps Institution of Oceanography and other institutions have developed GOFLOW, a deep learning method that uses existing weather satellite thermal imagery to create high-resolution hourly maps of ocean surface currents. The technique allows for the observation of small-scale currents and vertical mixing that were previously only accessible via computer simulations.
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What happened
Deep learning turns weather satellite thermal imagery into hourly ocean current maps Gaby Clark Scientific Editor Robert Egan Associate Editor Scientists have developed a new method to measure ocean surface currents over large areas in greater detail than…
Why it matters
Called GOFLOW (Geostationary Ocean Flow), the approach applies deep learning to thermal images from weather satellites already in orbit, requiring no new hardware to achieve what the researchers describe as a major advancement in ocean observation.
Common ground
The study, co-led by Luc Lenain, an oceanographer at UC San Diego's Scripps Institution of Oceanography, and Kaushik Srinivasan, a Scripps alumnus now at UCLA, was published today in the journal Nature Geoscience.
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Researchers from Scripps Institution of Oceanography and other institutions have developed GOFLOW, a deep learning method that uses existing weather satellite thermal imagery to create high-resolution hourly maps of ocean surface currents. The technique allows for the observation of small-scale currents and vertical mixing that were previously only accessible via computer simulations.
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fact_checkClaims Checked
eFinder analyzed this article and checked 10 claims against available evidence, cross-references, web search, and Wikipedia. Here is what the fact-checking layer found.
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