Environmental Internship Program - 2023 Booklet
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Maya Avida ’26<br />
PHYSICS<br />
Certificates: Statistics and Machine Learning,<br />
Sustainable Energy<br />
OCEANS AND<br />
ATMOSPHERE<br />
PROJECT TITLE<br />
Deep Learning for<br />
Prediction of Ocean<br />
Turbulence<br />
ORGANIZATION(S)<br />
School of Oceanography,<br />
University of Washington<br />
LOCATION(S)<br />
Seattle, Washington<br />
MENTOR(S)<br />
Georgy Manucharyan,<br />
Assistant Professor,<br />
School of Oceanography,<br />
University of Washington;<br />
Scott Martin, Ph.D.<br />
candidate, School of<br />
Oceanography, University<br />
of Washington<br />
Sea surface height (SSH) is a critical metric<br />
for understanding ocean eddies and currents.<br />
However, satellites are only able to measure<br />
SSH in one dimension, along the track in<br />
which they pass over the ocean. The standard<br />
method of estimating ocean currents uses<br />
optimal interpolation, which is an imperfect<br />
deterministic statistical method. This year,<br />
my research mentor Scott Martin published a<br />
paper demonstrating a new method for gridding<br />
SSH that significantly outperformed optimal<br />
interpolation by using machine learning to<br />
synthesize observations of SSH and sea surface<br />
temperature. My project used this new model<br />
to predict sea surface height up to 30 days into<br />
the future from raw satellite data. Being able to<br />
predict SSH forward in time would be very useful<br />
to oceanographers for a broad range of benefits,<br />
for example aiding oceanographers during field<br />
research by predicting the location of future<br />
eddies.<br />
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